Summary of Icdpo: Effectively Borrowing Alignment Capability Of Others Via In-context Direct Preference Optimization, by Feifan Song et al.
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
by Feifan Song, Yuxuan Fan, Xin Zhang, Peiyi Wang, Houfeng Wang
First submitted to arxiv on: 14 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 paper proposes a novel approach called In-Context Direct Preference Optimization (ICDPO) that enhances the performance of Large Language Models (LLMs) without requiring fine-tuning. The method leverages the states of LLMs before and after In-context Learning (ICL) to derive an instant scorer, which estimates well-aligned responses generated by superior LLMs with ICL. This approach can be further improved using a two-stage retriever and upgraded scorer. Experimental results show that ICDPO outperforms fine-tuning-free baselines and is competitive with SFT + LoRA. The paper also provides detailed analyses to offer insights into the effectiveness of ICDPO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps Large Language Models (LLMs) generate better responses without needing a lot of extra work. The method, called In-Context Direct Preference Optimization (ICDPO), uses information from the LLM before and after it learns new things to make better predictions. This approach can be improved further by adding more steps. The results show that ICDPO is very effective and works as well as other approaches that require more effort. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Optimization
