Summary of Laffi: Leveraging Hybrid Natural Language Feedback For Fine-tuning Language Models, by Qianxi Li et al.
LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models
by Qianxi Li, Yingyue Cao, Jikun Kang, Tianpei Yang, Xi Chen, Jun Jin, Matthew E. Taylor
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper introduces an innovative approach to fine-tuning Large Language Models (LLMs) for specific downstream tasks, called Natural Language Feedback for Finetuning LLMs (LaFFi). The authors aim to address the limitations of Supervised Fine-Tuning (SFT), which sometimes produces simple mistakes and hallucinations on reasoning tasks like question-answering. LaFFi requires LLMs to directly predict the feedback they will receive from an annotator, significantly improving accuracy in in-domain question-answering tasks. The paper also explores the impact of human-annotated data proportion on fine-tuning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make computers better at answering questions by fine-tuning large language models. Normally, these models can get confused and give wrong answers. The new method, called LaFFi, makes the model think about what it will be told is correct or not, which improves its accuracy in answering questions correctly. This could lead to more accurate computers that help us with tasks like searching for information. |
Keywords
* Artificial intelligence * Fine tuning * Question answering * Supervised
