Summary of Unveiling the Impact Of Coding Data Instruction Fine-tuning on Large Language Models Reasoning, by Xinlu Zhang et al.
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
by Xinlu Zhang, Zhiyu Zoey Chen, Xi Ye, Xianjun Yang, Lichang Chen, William Yang Wang, Linda Ruth Petzold
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Instruction Fine-Tuning (IFT) method significantly enhances the zero-shot capabilities of pretrained Large Language Models (LLMs). By analyzing the impact of coding data on LLMs’ reasoning capacities during the IFT stage, this paper provides valuable insights into how coding data affects model performance across different domains and tasks. The study fine-tunes six LLM backbones across various families and scales, evaluating their performance across twelve tasks in three reasoning domains. Results show that coding data tuning enhances overall reasoning capabilities, with consistent trends within each domain and comparable task-specific benefits across model families. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can learn to reason better when given extra instructions or “coding data”. This helps them make decisions and solve problems more effectively. The researchers tested how well this worked by giving different amounts of coding data to six types of language models, then seeing how they did on 12 tasks in three areas: math, science, and language. They found that giving the right amount of coding data made a big difference – it helped the models get better at reasoning overall, and even more importantly, at specific tasks like solving math problems or understanding scientific concepts. |
Keywords
» Artificial intelligence » Fine tuning » Zero shot
