Scheduled Commit

data/clustering_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -0,0 +1 @@
+{"tstamp": 1722465648.9957, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "7ffe7dac8e644f5a970652a0d40ebad6", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["North America", "Antarctica", "Asia", "South America", "Europe", "Australia", "Africa", "top hat", "cowboy hat", "beret", "silk", "denim", "linen", "cotton", "leather", "polyester", "wool"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "a410501fd5954b768b52dda204d4868d", "1_model_name": "text-embedding-3-large", "1_prompt": ["North America", "Antarctica", "Asia", "South America", "Europe", "Australia", "Africa", "top hat", "cowboy hat", "beret", "silk", "denim", "linen", "cotton", "leather", "polyester", "wool"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -1,2 +1,4 @@
 {"tstamp": 1722451741.9841, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722451711.9569, "finish": 1722451741.9841, "ip": "", "conv_id": "4e6152cfcb424536b8c494bb2ce8be73", "model_name": "text-embedding-004", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722451741.9841, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722451711.9569, "finish": 1722451741.9841, "ip": "", "conv_id": "962bddddb9da48c59856ddeb029c1f57", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722465608.9748, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722465608.2091, "finish": 1722465608.9748, "ip": "", "conv_id": "7ffe7dac8e644f5a970652a0d40ebad6", "model_name": "GritLM/GritLM-7B", "prompt": ["North America", "Antarctica", "Asia", "South America", "Europe", "Australia", "Africa", "top hat", "cowboy hat", "beret", "silk", "denim", "linen", "cotton", "leather", "polyester", "wool"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722465608.9748, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722465608.2091, "finish": 1722465608.9748, "ip": "", "conv_id": "a410501fd5954b768b52dda204d4868d", "model_name": "text-embedding-3-large", "prompt": ["North America", "Antarctica", "Asia", "South America", "Europe", "Australia", "Africa", "top hat", "cowboy hat", "beret", "silk", "denim", "linen", "cotton", "leather", "polyester", "wool"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -78,3 +78,7 @@
 {"tstamp": 1722462601.8586, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "98c3f4db315a4b218adca6965ca64bcc", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "do any headphones work with ps4?", "0_output": [["do any headphones work with ps4?", "Gaming Stackexchange\n\nQ: Can I use my regular 3.5mm headphones with a PS4? Can I use my personal regular 3.5mm headphones with a PS4 to listen to in game audio through the Dual Shock or remotes?\n\nA: I found the steps slightly different to the answer from @Teryx, maybe due to a later software update? This worked for system software 2.04:\nFirst plug in your headphones to the DS4. Then:\nSettings > Devices > Audio Devices > Output Device.\nThat should be set to \"Headphones Connected to Controller\".\nAs @Teryx said, on the same screen, > Output to Headphones should be set to \"All Audio\"\n"]], "0_corpus": "stackexchange", "1_conv_id": "8aa6d65cbc8a4a6b8d6f48dc69c02775", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "do any headphones work with ps4?", "1_output": [["do any headphones work with ps4?", "Gaming Stackexchange\n\nQ: Can I use my regular 3.5mm headphones with a PS4? Can I use my personal regular 3.5mm headphones with a PS4 to listen to in game audio through the Dual Shock or remotes?\n\nA: I found the steps slightly different to the answer from @Teryx, maybe due to a later software update? This worked for system software 2.04:\nFirst plug in your headphones to the DS4. Then:\nSettings > Devices > Audio Devices > Output Device.\nThat should be set to \"Headphones Connected to Controller\".\nAs @Teryx said, on the same screen, > Output to Headphones should be set to \"All Audio\"\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722465420.3747, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f2dcbd95ca2f4745afd2dfdb70e3e51b", "0_model_name": "BM25", "0_prompt": "1 what is the relationship between the variance and the standard deviation?", "0_output": [["1 what is the relationship between the variance and the standard deviation?", "Stats Stackexchange\n\nQ: What's the difference between variance and standard deviation? I was wondering what the difference between the variance and the standard deviation is. \nIf you calculate the two values, it is clear that you get the standard deviation out of the variance, but what does that mean in terms of the distribution you are observing?\nFurthermore, why do you really need a standard deviation?\n\nA: You don't need both.  They each have different purposes.  The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically.  For example, the sum of uncorrelated distributions (random variables) also has a variance that is the sum of the variances of those distributions.  This wouldn't be true of the SD.  On the other hand, the SD has the convenience of being expressed in units of the original variable.\n"]], "0_corpus": "stackexchange", "1_conv_id": "9966eed1d8cc4384a35298d4896ee1cd", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "1 what is the relationship between the variance and the standard deviation?", "1_output": [["1 what is the relationship between the variance and the standard deviation?", "Stats Stackexchange\n\nQ: What's the difference between variance and standard deviation? I was wondering what the difference between the variance and the standard deviation is. \nIf you calculate the two values, it is clear that you get the standard deviation out of the variance, but what does that mean in terms of the distribution you are observing?\nFurthermore, why do you really need a standard deviation?\n\nA: You don't need both.  They each have different purposes.  The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically.  For example, the sum of uncorrelated distributions (random variables) also has a variance that is the sum of the variances of those distributions.  This wouldn't be true of the SD.  On the other hand, the SD has the convenience of being expressed in units of the original variable.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722465434.2947, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "acb5cb0d249949e28247857d702e7bc3", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "cb703ba5652e43eabaa1ebd424ccad26", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722465711.4368, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "0ef40f5333d1494cb3a1f557fa0c7910", "0_model_name": "voyage-multilingual-2", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "0_corpus": "stackexchange", "1_conv_id": "b081fae2f0c64992aef76a9162aa76f0", "1_model_name": "BM25", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.\nI know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.\nHowever what other usages are there for __name__?\n\nA: __name__ is \"__main__\" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.\nfoo.py:\nprint(__name__)\n\nbar.py \nimport foo\n\nRun the scripts:\n$ python foo.py\n__main__\n$ python bar.py \nfoo\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722465740.4392, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "a58d1265cbf44a65a135c79b9ae1e7c6", "0_model_name": "text-embedding-004", "0_prompt": "who is young george bailey in it's a wonderful life", "0_output": [["who is young george bailey in it's a wonderful life", "George Bailey (It's a Wonderful Life)\n\nEarly life In the winter of 1919, George (aged 12, played by Bobby Anderson) and his friends Bert, Ernie Bishop, Marty Hatch, Sam Wainwright, and his brother Harry are sledding on a frozen river. Harry breaks through the ice and George jumps into the freezing water to save him. In doing so, George became ill with an infection that waylaid him for some time and caused him to lose hearing in his left ear.\nIn May 1920, George returns to his job at Mr. Gower's drugstore, where he first attends to the soda fountain when two customers are a young Mary Hatch and her friend Violet Bick. George announces his plan to be an explorer and travel the world, citing National Geographic as an inspiration where Mary whispers her love for George in his deaf ear. George then finds a telegram informing Gower that his son has died in the Spanish flu pandemic. A visibly distraught Gower directs George to deliver medicine to a customer, but George realizes that, in his distress, he had inadvertently put poison into the capsules. He seeks advice from his father, who is president of the Bailey Brothers Building & Loan, but his father is meeting with Henry F. Potter, one of the shareholders. When he returns to the store, Gower angrily berates him for not delivering the capsules, until George blurts out Gower's mistake. Realizing this would have been fatal, Gower tearfully thanks George, who promises that he will never tell anyone what happened."]], "0_corpus": "wikipedia", "1_conv_id": "f6e43c4882b04dc590789a91e10f844a", "1_model_name": "text-embedding-3-large", "1_prompt": "who is young george bailey in it's a wonderful life", "1_output": [["who is young george bailey in it's a wonderful life", "George Bailey (It's a Wonderful Life)\n\nGeorge Bailey is a fictional character and the protagonist in Frank Capra's 1946 film It's a Wonderful Life. The character is a building and loan banker who sacrifices his dreams in order to help his community of Bedford Falls to the point where he feels life has passed him by. Eventually, due to difficulties in keeping the building and loan solvent, Bailey falls into despair so deep that he contemplates suicide, until a guardian angel, Clarence Odbody, gives him a valuable perspective on the worth of his life. George finds through Odbody's angelic power and gift what life would be like if he didn't have his wife, Mary, his children and friends, and what their lives and the social structure of Bedford Falls would be like without him.\nBailey is played by James Stewart as an adult and Bobby Anderson as a child, and is loosely based on George Pratt, a character in Philip Van Doren Stern's 1943 booklet The Greatest Gift.\nThe character was portrayed by Pete Davidson in a 2020 television special.\nFictional biography"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722465759.6674, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "c68abe75c7294b6d8735fc7c95541249", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "0_output": [["Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "Title: Stellarator News, Issue 38, March 1995\n\nAbstract: Stellarator News, an international journal of the stellarator community, is Published by Fusion Energy Division, Oak Ridge National Laboratory, James A. Rome, Editor In the March 1995 issue . . . **** Exerpts from the U.S. Congress Office of Technology Assment report on TPX and Alternate Concepts. **** Edge transport and turbulence studies on U-3M The turbulent-driven particle flow is shown to be comparable with the equilibrium flow at the boundary of the configuration under Alfven-heating conditions in the U-3M torsatron. **** Topological aspects of island divertor studies on W7-AS The structure of the edge plasma in W7-AS, observed with probes, television camera, and H-alpha light agrees at low beta with vacuum field calculations: the low-valued resonances at iotabar=5/m are resolved for m = 8 to 11; external perturbations are not significant at the edge, even for iotabar = 5/10. **** 140-GHz second harmonic O-mode electron cyclotron heating at W7-AS First experimental results are presented of 140-GHz second harmonic O-mode (0.7-MW power) ECRH at a high electron density of 1.8 x 10^20 m^-3 on W7-AS. **** Equilibrium plasma currents in quasi-symmetric stellarators with a helical magnetic axis The plasma currents in quasi-symmetric stellarators are calculated to determe the quasi-symmetric conditions with the help of analytical equations."]], "0_corpus": "arxiv", "1_conv_id": "935a0a0836b6482e91837f3442dda66e", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "1_output": [["Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "Title: On Transferability of Prompt Tuning for Natural Language Processing\n\nAbstract: Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts. However, PT requires much more training time than fine-tuning. Intuitively, knowledge transfer can help to improve the efficiency. To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work. We find that (1) in zero-shot setting, trained soft prompts can effectively transfer to similar tasks on the same PLM and also to other PLMs with a cross-model projector trained on similar tasks; (2) when used as initialization, trained soft prompts of similar tasks and projected prompts of other PLMs can significantly accelerate training and also improve the performance of PT. Moreover, to explore what decides prompt transferability, we investigate various transferability indicators and find that the overlapping rate of activated neurons strongly reflects the transferability, which suggests how the prompts stimulate PLMs is essential. Our findings show that prompt transfer is promising for improving PT, and further research shall focus more on prompts' stimulation to PLMs. The source code can be obtained from https://github.com/thunlp/Prompt-Transferability."]], "1_corpus": "arxiv"}
+{"tstamp": 1722465812.3109, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "0c6201bc4ecc4404938c5684f907f8b5", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Research showing safety and factual grounding improvements in large-language dialog models.", "0_output": [["Research showing safety and factual grounding improvements in large-language dialog models.", "Title: Towards Harnessing Large Language Models for Comprehension of Conversational Grounding\n\nAbstract: Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeking conversations to investigate the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks and discuss ongoing research efforts to enhance large language model-based conversational grounding comprehension through pipeline architectures and knowledge bases. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations."]], "0_corpus": "arxiv", "1_conv_id": "ff0a091aac284d1a99206a814ef2a352", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "Research showing safety and factual grounding improvements in large-language dialog models.", "1_output": [["Research showing safety and factual grounding improvements in large-language dialog models.", "Title: Chaos or Noise - Difficulties of a Distinction\n\nAbstract: In experiments, the dynamical behavior of systems is reflected in time series. Due to the finiteness of the observational data set it is not possible to reconstruct the invariant measure up to arbitrary fine resolution and arbitrary high embedding dimension. These restrictions limit our ability to distinguish between signals generated by different systems, such as regular, chaotic or stochastic ones, when analyzed from a time series point of view. We propose to classify the signal behavior, without referring to any specific model, as stochastic or deterministic on a certain scale of the resolution $\\epsilon$, according to the dependence of the $(\\epsilon,\\tau)$-entropy, $h(\\epsilon, \\tau)$, and of the finite size Lyapunov exponent, $\\lambda(\\epsilon)$, on $\\epsilon$."]], "1_corpus": "arxiv"}

data/retrieval_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -203,3 +203,15 @@
 {"tstamp": 1722465408.8588, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722465408.4342, "finish": 1722465408.8588, "ip": "", "conv_id": "9966eed1d8cc4384a35298d4896ee1cd", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "1 what is the relationship between the variance and the standard deviation?", "output": [["1 what is the relationship between the variance and the standard deviation?", "Stats Stackexchange\n\nQ: What's the difference between variance and standard deviation? I was wondering what the difference between the variance and the standard deviation is. \nIf you calculate the two values, it is clear that you get the standard deviation out of the variance, but what does that mean in terms of the distribution you are observing?\nFurthermore, why do you really need a standard deviation?\n\nA: You don't need both.  They each have different purposes.  The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically.  For example, the sum of uncorrelated distributions (random variables) also has a variance that is the sum of the variances of those distributions.  This wouldn't be true of the SD.  On the other hand, the SD has the convenience of being expressed in units of the original variable.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722465429.8448, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722465429.6376, "finish": 1722465429.8448, "ip": "", "conv_id": "acb5cb0d249949e28247857d702e7bc3", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
 {"tstamp": 1722465429.8448, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722465429.6376, "finish": 1722465429.8448, "ip": "", "conv_id": "cb703ba5652e43eabaa1ebd424ccad26", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "corpus": "wikipedia"}
+{"tstamp": 1722465581.8578, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722465581.5032, "finish": 1722465581.8578, "ip": "", "conv_id": "bfb0d994dbc14fe49555e33a17e1070e", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Using Rhetorical Structure Theory and language models for argument mining", "output": [["Using Rhetorical Structure Theory and language models for argument mining", "Title: End-to-End Argument Mining over Varying Rhetorical Structures\n\nAbstract: Rhetorical Structure Theory implies no single discourse interpretation of a text, and the limitations of RST parsers further exacerbate inconsistent parsing of similar structures. Therefore, it is important to take into account that the same argumentative structure can be found in semantically similar texts with varying rhetorical structures. In this work, the differences between paraphrases within the same argument scheme are evaluated from a rhetorical perspective. The study proposes a deep dependency parsing model to assess the connection between rhetorical and argument structures. The model utilizes rhetorical relations; RST structures of paraphrases serve as training data augmentations. The method allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence. It is evaluated on the bilingual Microtexts corpus, and the first results on fully-fledged argument parsing for the Russian version of the corpus are reported. The results suggest that argument mining can benefit from multiple variants of discourse structure."]], "corpus": "arxiv"}
+{"tstamp": 1722465581.8578, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722465581.5032, "finish": 1722465581.8578, "ip": "", "conv_id": "75c5bcc548c9425a942dc2cfa4defc6d", "model_name": "voyage-multilingual-2", "prompt": "Using Rhetorical Structure Theory and language models for argument mining", "output": [["Using Rhetorical Structure Theory and language models for argument mining", "Title: End-to-End Argument Mining over Varying Rhetorical Structures\n\nAbstract: Rhetorical Structure Theory implies no single discourse interpretation of a text, and the limitations of RST parsers further exacerbate inconsistent parsing of similar structures. Therefore, it is important to take into account that the same argumentative structure can be found in semantically similar texts with varying rhetorical structures. In this work, the differences between paraphrases within the same argument scheme are evaluated from a rhetorical perspective. The study proposes a deep dependency parsing model to assess the connection between rhetorical and argument structures. The model utilizes rhetorical relations; RST structures of paraphrases serve as training data augmentations. The method allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence. It is evaluated on the bilingual Microtexts corpus, and the first results on fully-fledged argument parsing for the Russian version of the corpus are reported. The results suggest that argument mining can benefit from multiple variants of discourse structure."]], "corpus": "arxiv"}
+{"tstamp": 1722465702.8873, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722465702.4912, "finish": 1722465702.8873, "ip": "", "conv_id": "0ef40f5333d1494cb3a1f557fa0c7910", "model_name": "voyage-multilingual-2", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722465702.8873, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722465702.4912, "finish": 1722465702.8873, "ip": "", "conv_id": "b081fae2f0c64992aef76a9162aa76f0", "model_name": "BM25", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.\nI know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.\nHowever what other usages are there for __name__?\n\nA: __name__ is \"__main__\" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.\nfoo.py:\nprint(__name__)\n\nbar.py \nimport foo\n\nRun the scripts:\n$ python foo.py\n__main__\n$ python bar.py \nfoo\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722465719.9702, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722465719.3544, "finish": 1722465719.9702, "ip": "", "conv_id": "a58d1265cbf44a65a135c79b9ae1e7c6", "model_name": "text-embedding-004", "prompt": "who is young george bailey in it's a wonderful life", "output": [["who is young george bailey in it's a wonderful life", "George Bailey (It's a Wonderful Life)\n\nEarly life In the winter of 1919, George (aged 12, played by Bobby Anderson) and his friends Bert, Ernie Bishop, Marty Hatch, Sam Wainwright, and his brother Harry are sledding on a frozen river. Harry breaks through the ice and George jumps into the freezing water to save him. In doing so, George became ill with an infection that waylaid him for some time and caused him to lose hearing in his left ear.\nIn May 1920, George returns to his job at Mr. Gower's drugstore, where he first attends to the soda fountain when two customers are a young Mary Hatch and her friend Violet Bick. George announces his plan to be an explorer and travel the world, citing National Geographic as an inspiration where Mary whispers her love for George in his deaf ear. George then finds a telegram informing Gower that his son has died in the Spanish flu pandemic. A visibly distraught Gower directs George to deliver medicine to a customer, but George realizes that, in his distress, he had inadvertently put poison into the capsules. He seeks advice from his father, who is president of the Bailey Brothers Building & Loan, but his father is meeting with Henry F. Potter, one of the shareholders. When he returns to the store, Gower angrily berates him for not delivering the capsules, until George blurts out Gower's mistake. Realizing this would have been fatal, Gower tearfully thanks George, who promises that he will never tell anyone what happened."]], "corpus": "wikipedia"}
+{"tstamp": 1722465719.9702, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722465719.3544, "finish": 1722465719.9702, "ip": "", "conv_id": "f6e43c4882b04dc590789a91e10f844a", "model_name": "text-embedding-3-large", "prompt": "who is young george bailey in it's a wonderful life", "output": [["who is young george bailey in it's a wonderful life", "George Bailey (It's a Wonderful Life)\n\nGeorge Bailey is a fictional character and the protagonist in Frank Capra's 1946 film It's a Wonderful Life. The character is a building and loan banker who sacrifices his dreams in order to help his community of Bedford Falls to the point where he feels life has passed him by. Eventually, due to difficulties in keeping the building and loan solvent, Bailey falls into despair so deep that he contemplates suicide, until a guardian angel, Clarence Odbody, gives him a valuable perspective on the worth of his life. George finds through Odbody's angelic power and gift what life would be like if he didn't have his wife, Mary, his children and friends, and what their lives and the social structure of Bedford Falls would be like without him.\nBailey is played by James Stewart as an adult and Bobby Anderson as a child, and is loosely based on George Pratt, a character in Philip Van Doren Stern's 1943 booklet The Greatest Gift.\nThe character was portrayed by Pete Davidson in a 2020 television special.\nFictional biography"]], "corpus": "wikipedia"}
+{"tstamp": 1722465750.4278, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722465748.3014, "finish": 1722465750.4278, "ip": "", "conv_id": "c68abe75c7294b6d8735fc7c95541249", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "output": [["Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "Title: Stellarator News, Issue 38, March 1995\n\nAbstract: Stellarator News, an international journal of the stellarator community, is Published by Fusion Energy Division, Oak Ridge National Laboratory, James A. Rome, Editor In the March 1995 issue . . . **** Exerpts from the U.S. Congress Office of Technology Assment report on TPX and Alternate Concepts. **** Edge transport and turbulence studies on U-3M The turbulent-driven particle flow is shown to be comparable with the equilibrium flow at the boundary of the configuration under Alfven-heating conditions in the U-3M torsatron. **** Topological aspects of island divertor studies on W7-AS The structure of the edge plasma in W7-AS, observed with probes, television camera, and H-alpha light agrees at low beta with vacuum field calculations: the low-valued resonances at iotabar=5/m are resolved for m = 8 to 11; external perturbations are not significant at the edge, even for iotabar = 5/10. **** 140-GHz second harmonic O-mode electron cyclotron heating at W7-AS First experimental results are presented of 140-GHz second harmonic O-mode (0.7-MW power) ECRH at a high electron density of 1.8 x 10^20 m^-3 on W7-AS. **** Equilibrium plasma currents in quasi-symmetric stellarators with a helical magnetic axis The plasma currents in quasi-symmetric stellarators are calculated to determe the quasi-symmetric conditions with the help of analytical equations."]], "corpus": "arxiv"}
+{"tstamp": 1722465750.4278, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722465748.3014, "finish": 1722465750.4278, "ip": "", "conv_id": "935a0a0836b6482e91837f3442dda66e", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "output": [["Investigating the transferability of soft prompts across different PLMs and its impact on training time.", "Title: On Transferability of Prompt Tuning for Natural Language Processing\n\nAbstract: Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts. However, PT requires much more training time than fine-tuning. Intuitively, knowledge transfer can help to improve the efficiency. To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work. We find that (1) in zero-shot setting, trained soft prompts can effectively transfer to similar tasks on the same PLM and also to other PLMs with a cross-model projector trained on similar tasks; (2) when used as initialization, trained soft prompts of similar tasks and projected prompts of other PLMs can significantly accelerate training and also improve the performance of PT. Moreover, to explore what decides prompt transferability, we investigate various transferability indicators and find that the overlapping rate of activated neurons strongly reflects the transferability, which suggests how the prompts stimulate PLMs is essential. Our findings show that prompt transfer is promising for improving PT, and further research shall focus more on prompts' stimulation to PLMs. The source code can be obtained from https://github.com/thunlp/Prompt-Transferability."]], "corpus": "arxiv"}
+{"tstamp": 1722465800.677, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722465798.5346, "finish": 1722465800.677, "ip": "", "conv_id": "0c6201bc4ecc4404938c5684f907f8b5", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Research showing safety and factual grounding improvements in large-language dialog models.", "output": [["Research showing safety and factual grounding improvements in large-language dialog models.", "Title: Towards Harnessing Large Language Models for Comprehension of Conversational Grounding\n\nAbstract: Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeking conversations to investigate the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks and discuss ongoing research efforts to enhance large language model-based conversational grounding comprehension through pipeline architectures and knowledge bases. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations."]], "corpus": "arxiv"}
+{"tstamp": 1722465800.677, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722465798.5346, "finish": 1722465800.677, "ip": "", "conv_id": "ff0a091aac284d1a99206a814ef2a352", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Research showing safety and factual grounding improvements in large-language dialog models.", "output": [["Research showing safety and factual grounding improvements in large-language dialog models.", "Title: Chaos or Noise - Difficulties of a Distinction\n\nAbstract: In experiments, the dynamical behavior of systems is reflected in time series. Due to the finiteness of the observational data set it is not possible to reconstruct the invariant measure up to arbitrary fine resolution and arbitrary high embedding dimension. These restrictions limit our ability to distinguish between signals generated by different systems, such as regular, chaotic or stochastic ones, when analyzed from a time series point of view. We propose to classify the signal behavior, without referring to any specific model, as stochastic or deterministic on a certain scale of the resolution $\\epsilon$, according to the dependence of the $(\\epsilon,\\tau)$-entropy, $h(\\epsilon, \\tau)$, and of the finite size Lyapunov exponent, $\\lambda(\\epsilon)$, on $\\epsilon$."]], "corpus": "arxiv"}
+{"tstamp": 1722465821.0267, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722465818.879, "finish": 1722465821.0267, "ip": "", "conv_id": "5118102b23204134bea5a72c788f7ef0", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "are illusion and hallucination the same?", "output": [["are illusion and hallucination the same?", "English Stackexchange\n\nQ: Difference between 'hallucination' and 'illusion' The following quote is found on The Basics of Philosophy page:\n\nRepresentationalists argue their case from the \"epistemological fact\" that it is impossible to have experience beyond the sensory surface, from the fact that dreams, hallucinations and visual illusions clearly indicate that the world of experience is not the same thing as the world itself,\n\nWhat is the difference between the terms \"hallucination\" and \"visual illusion\", as used in the quote?\n\nA: A hallucination is to see something that is not real! For example, if you feel that you are seeing a cat that is flying, then you are experiencing a hallucination.\nAn illusion is to look at something real but to see it in a different way, like when you look at two horizontal lines but it feels like they are not actually horizontal.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722465821.0267, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722465818.879, "finish": 1722465821.0267, "ip": "", "conv_id": "1563065f582143bda784b2e284059c97", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are illusion and hallucination the same?", "output": [["are illusion and hallucination the same?", "Stackoverflow Stackexchange\n\nQ: Why is AWS CloudFormer so slow even if i beef up the underlying instance? Using CloudFormer to Create AWS CloudFormation Templates from Existing AWS Resources is a resource that I hoped to use to take frequent and scheduled infrastructure snapshots as a mechanism for \"Disaster Recovery\". \nThe cloudformer tool is a web-based app that is spun up as an EC2 instance by an AWS Cloudformation canned template. \nIt takes upto 20-30 minutes to create a template file that contains all the AWS resources in my account. Why?\nI have tried increasing the instance size from a t2.small to a m5.4xlarge and there is only marginal performance improvement. \nIs it because the cloudformer tool makes a ton of calls to aws-cli and these calls are somehow throttled by AWS?\nAny alternatives to automate taking scheduled snapshots of AWS infrastructure that can be fed into Cloud Formation for quick recovery?\nThanks!\n"]], "corpus": "stackexchange"}

data/sts_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -0,0 +1 @@
+{"tstamp": 1722465714.8046, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a6c0e6ac5ad441c7ac77bef9c399c40b", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_txt0": "She trained a neural network to recognize faces.", "0_txt1": "She developed an AI to identify human features.", "0_txt2": "She trained a new recruit to recognize faces.", "0_output": "", "1_conv_id": "c982e91e50d84cd3a8f98b88d36ebeb0", "1_model_name": "BAAI/bge-large-en-v1.5", "1_txt0": "She trained a neural network to recognize faces.", "1_txt1": "She developed an AI to identify human features.", "1_txt2": "She trained a new recruit to recognize faces.", "1_output": ""}

data/sts_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -0,0 +1,4 @@
+{"tstamp": 1722465669.846, "task_type": "sts", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722465667.5759, "finish": 1722465669.846, "ip": "", "conv_id": "414bea7d1ce7412eba9dbe44a7216324", "model_name": "text-embedding-3-large", "txt0": "Three women are sitting on a blanket.", "txt1": "Two men are sitting down.", "txt2": "Two bearded men sit side by side.", "output": ""}
+{"tstamp": 1722465669.846, "task_type": "sts", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722465667.5759, "finish": 1722465669.846, "ip": "", "conv_id": "65ad6423f22f4451b7e1e2bf750d09b0", "model_name": "Salesforce/SFR-Embedding-2_R", "txt0": "Three women are sitting on a blanket.", "txt1": "Two men are sitting down.", "txt2": "Two bearded men sit side by side.", "output": ""}
+{"tstamp": 1722465680.2296, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722465680.2018, "finish": 1722465680.2296, "ip": "", "conv_id": "a6c0e6ac5ad441c7ac77bef9c399c40b", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "She trained a neural network to recognize faces.", "txt1": "She developed an AI to identify human features.", "txt2": "She trained a new recruit to recognize faces.", "output": ""}
+{"tstamp": 1722465680.2296, "task_type": "sts", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722465680.2018, "finish": 1722465680.2296, "ip": "", "conv_id": "c982e91e50d84cd3a8f98b88d36ebeb0", "model_name": "BAAI/bge-large-en-v1.5", "txt0": "She trained a neural network to recognize faces.", "txt1": "She developed an AI to identify human features.", "txt2": "She trained a new recruit to recognize faces.", "output": ""}