Summary of Context-dpo: Aligning Language Models For Context-faithfulness, by Baolong Bi et al.
Context-DPO: Aligning Language Models for Context-Faithfulness
by Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 abstract proposes a novel alignment method, Context-DPO, to enhance the context-faithfulness of large language models (LLMs). The authors introduce ConFiQA, a benchmark simulating Retrieval-Augmented Generation scenarios with knowledge conflicts. They demonstrate that their approach significantly improves context-faithfulness by 35% to 280%, while preserving LLMs’ generative capabilities and providing interpretable insights into context utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to make large language models more reliable by helping them understand user instructions better. It suggests a new way to align the model with what humans mean, which is important for getting accurate answers. The researchers test their method on popular language models and show that it works well, improving the accuracy of responses. They also provide code and data so others can use this approach. |
Keywords
» Artificial intelligence » Alignment » Retrieval augmented generation
