Summary of Is In-context Learning Sufficient For Instruction Following in Llms?, by Hao Zhao et al.
Is In-Context Learning Sufficient for Instruction Following in LLMs?
by Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: This research paper explores the capabilities of In-context learning (ICL) for Long-Short Term Memory Language Models (LLMs). The authors investigate the alignment method URIAL, which uses only three in-context examples to achieve non-trivial instruction following performance. However, they find that ICL alignment with URIAL underperforms compared to instruction fine-tuning on the MT-Bench benchmark, especially for more capable base LLMs. The study uncovers the crucial role of decoding parameters and proposes improving the approach by adding high-quality demonstrations via greedy search. Furthermore, the paper provides a systematic comparison between ICL and instruction fine-tuning (IFT) in the low data regime, where ICL can be a viable alternative to IFT. The authors conclude that their work advances the understanding of ICL as an alignment technique and its relationship to IFT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study looks at how language models can learn from examples without changing their weights. Researchers tested a method called URIAL, which uses just three examples to make language models follow instructions well. However, they found that this method didn’t work as well as another way of teaching language models, called instruction fine-tuning. The study figured out what makes the alignment method work best and how to improve it. They also compared these two methods in situations where there’s not much data available, and found that the alignment method can be a good alternative. |
Keywords
» Artificial intelligence » Alignment » Fine tuning
