Summary of A Closer Look at the Limitations Of Instruction Tuning, by Sreyan Ghosh and Chandra Kiran Reddy Evuru and Sonal Kumar and Ramaneswaran S and Deepali Aneja and Zeyu Jin and Ramani Duraiswami and Dinesh Manocha
A Closer Look at the Limitations of Instruction Tuning
by Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar, Ramaneswaran S, Deepali Aneja, Zeyu Jin, Ramani Duraiswami, Dinesh Manocha
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the limitations of Instruction Tuning (IT), a widely used method for transforming large language models into conversational agents. The study reveals that IT fails to enhance knowledge or skills in these models, instead leading to degradation or copying response patterns from limited datasets. Moreover, popular methods to improve IT do not yield performance gains over simple fine-tuning approaches. The findings suggest that responses generated solely from pre-trained knowledge outperform those learned through IT on open-source datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IT is a technique for training large language models using instruction-response pairs. While it has achieved success and widespread adoption, its limitations remain unexplored. This paper reveals the shortcomings of IT by analyzing changes in language models through this process. The study shows that IT fails to enhance knowledge or skills, copying response patterns from datasets, leading to decreased quality, and increasing hallucinations by borrowing tokens from conceptually similar instances. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning
