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With the most recent workshop on Semantic Evaluation as a part of NAACL-2024, this year delighted to contribute with 🧪 on Chain-of-Thought fine-tuning concepts  to push forward LLMs reasoning capabilities in: 
With the most recent workshop on Semantic Evaluation as a part of NAACL-2024, this year delighted to contribute with 🧪 on Chain-of-Thought fine-tuning concepts  to push forward LLMs reasoning capabilities in: 
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🧪 1. Reading Comprehension of Numerals in texts  🇨🇳 
🧪 1. Reading Comprehension of Numerals in texts  🇨🇳 
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⭐ 
⭐ 
link
https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
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🔒 
🔒 
link
https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B
https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B
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🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
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⭐ 
⭐ 
link
https://github.com/nicolay-r/THOR-ECAC
https://github.com/nicolay-r/THOR-ECAC
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🔓 
🔓 
resource
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
model
nicolay-r/flan-t5-emotion-cause-thor-base
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
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resource
https://huggingface.co/papers/2404.03361
paper
2404.03361
https://huggingface.co/papers/2404.03361
nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion
  Cause in Conversations with Chain-of-Thought on Emotion States (2404.03361)
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🔑 In short, there are three major takeaways:
🔑 In short, there are three major takeaways:
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✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
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✅ 2. The language of the input data  matters in LLM reasoning capabilities: transfering data in English and picking English-based LLM is crucial for the most cases!
✅ 2. The language of the input data  matters in LLM reasoning capabilities: transfering data in English and picking English-based LLM is crucial for the most cases!
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✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠
✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠
With the most recent workshop on Semantic Evaluation as a part of NAACL-2024, this year delighted to contribute with 🧪 on Chain-of-Thought fine-tuning concepts  to push forward LLMs reasoning capabilities in: 

🧪 1. Reading Comprehension of Numerals in texts  🇨🇳 
⭐ https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
🔒 https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B 

🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
⭐ https://github.com/nicolay-r/THOR-ECAC
🔓 https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
 https://huggingface.co/papers/2404.03361

🔑 In short, there are three major takeaways:
✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
✅ 2. The language of the input data  matters in LLM reasoning capabilities: transfering data in English and picking English-based LLM is crucial for the most cases!
✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠