Summary of Dehallucinating Parallel Context Extension For Retrieval-augmented Generation, by Zexiong Ma et al.
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation
by Zexiong Ma, Shengnan An, Zeqi Lin, Yanzhen Zou, Jian-Guang Lou, Bing Xie
First submitted to arxiv on: 19 Dec 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 new approach called DePaC (Dehallucinating Parallel Context Extension) to address the issue of hallucinated information in large language models. Specifically, it focuses on alleviating two types of in-context hallucination: fact fabrication and fact omission. To achieve this, DePaC uses context-aware negative training to fine-tune the models and refuse to answer when contexts are not related to questions. Additionally, it proposes information-calibrated aggregation to prioritize context windows with higher information increment from their contexts. Experimental results on nine retrieval-augmented generation tasks demonstrate that DePaC significantly reduces hallucination and improves performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making large language models better at not making up false information. Right now, these models can sometimes provide answers that are not true or leave out important details. The researchers propose a new way to train the models so they don’t make these mistakes as often. They test their approach on nine different tasks and find that it works well, reducing the amount of fake information and improving the overall performance. |
Keywords
» Artificial intelligence » Hallucination » Retrieval augmented generation
