
### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*



L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.

We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.

Dataset list:
```
["coursera", "gsm100", "quality", "topic_retrieval_longchat", "tpo", "financial_qa", "gov_report_summ", "legal_contract_qa", "meeting_summ", "multidoc_qa", "narrative_qa", "natural_question", "news_summ", "paper_assistant", "patent_summ", "review_summ", "scientific_qa", "tv_show_summ"]
```

Detailed descriptions and how we collect the data can be found [https://github.com/OpenLMLab/LEval](https://github.com/OpenLMLab/LEval).
task_categories:summarization task_categories:question-answering task_categories:multiple-choice size_categories:1K<n<10K language:en license:gpl-3.0 Long_context region:us
{"language": ["en"], "license": "gpl-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization", "question-answering", "multiple-choice"], "viewer": true, "tags": ["Long_context"]}
TAGS
#task_categories-summarization #task_categories-question-answering #task_categories-multiple-choice #size_categories-1K<n<10K #language-English #license-gpl-3.0 #Long_context #region-us 


### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*



L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.

We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.

Dataset list:


Detailed descriptions and how we collect the data can be found URL
### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*



L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.

We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.

Dataset list:


Detailed descriptions and how we collect the data can be found URL
TAGS
#task_categories-summarization #task_categories-question-answering #task_categories-multiple-choice #size_categories-1K<n<10K #language-English #license-gpl-3.0 #Long_context #region-us 
 ### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*



L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.

We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.

Dataset list:


Detailed descriptions and how we collect the data can be found URL
passage: TAGS
#task_categories-summarization #task_categories-question-answering #task_categories-multiple-choice #size_categories-1K<n<10K #language-English #license-gpl-3.0 #Long_context #region-us 
### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*



L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.

We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.

Dataset list:


Detailed descriptions and how we collect the data can be found URL