Summary of Post-hoc Utterance Refining Method by Entity Mining For Faithful Knowledge Grounded Conversations, By Yoonna Jang et al.
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations
by Yoonna Jang, Suhyune Son, Jeongwoo Lee, Junyoung Son, Yuna Hur, Jungwoo Lim, Hyeonseok Moon, Kisu Yang, Heuiseok Lim
First submitted to arxiv on: 16 Jun 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 proposes a novel post-hoc refinement method, REM, to address the issue of hallucinations in language generation models. Specifically, it focuses on entity-level hallucination that can lead to critical misinformation and undesirable conversation. The proposed method refines generated utterances based on their source-faithfulness score, using key entities from the knowledge source to correct any inaccuracies. Experimental results demonstrate the effectiveness and adaptability of REM in reducing entity hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to solve a big problem with language generation models. Right now, these models can sometimes make up information that isn’t true or doesn’t come from the right place. This is especially important when we want conversations to be informed and helpful. The solution proposed by this paper helps fix these issues by looking at the source of the knowledge and making sure the generated text matches what’s actually known. It shows how well this works with examples, and makes the code available for others to use. |
Keywords
» Artificial intelligence » Hallucination
