content.raw: 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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content.value: 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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content.raw: 🧪 1. Reading Comprehension of Numerals in texts  🇨🇳 
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content.value: 🧪 1. Reading Comprehension of Numerals in texts  🇨🇳 
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content.raw: ⭐ 
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content.value: ⭐ 
content.href: https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
content.raw: https://github.com/GavinZhao19/SemEval24-NumAnalysis-CN
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content.raw: 🔒 
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content.value: 🔒 
content.href: https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B
content.raw: https://huggingface.co/GavinZhao23/NumAnalysis-Chatglm3-6B
content.type: link
content.type: text
content.type: new_line
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content.raw: 🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
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content.value: 🧪 2. Extracting Emotion-Causes using Reasoning Revision (RR)
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content.raw: ⭐ 
content.type: text
content.value: ⭐ 
content.href: https://github.com/nicolay-r/THOR-ECAC
content.raw: https://github.com/nicolay-r/THOR-ECAC
content.type: link
content.type: new_line
content.raw: 🔓 
content.type: text
content.value: 🔓 
content.raw: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
content.id: nicolay-r/flan-t5-emotion-cause-thor-base
content.type: model
content.type: resource
content.url: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
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content.label: 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)
content.raw: https://huggingface.co/papers/2404.03361
content.id: 2404.03361
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2404.03361
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content.raw: 🔑 In short, there are three major takeaways:
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content.value: 🔑 In short, there are three major takeaways:
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content.raw: ✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
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content.value: ✅ 1. The scale of the backboned LLM for SFT matters (>1.1B is preferable)
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content.raw: ✅ 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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content.value: ✅ 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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content.raw: ✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠
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content.value: ✅ 3. CoT and RR takes more time ⏳ for inferring and fine-tuning, proportionally to abount of steps in chain / amount of revisions in reasoning 🧠
rawContent: 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 🧠