Summary of Diahalu: a Dialogue-level Hallucination Evaluation Benchmark For Large Language Models, by Kedi Chen and Qin Chen and Jie Zhou and Yishen He and Liang He
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
by Kedi Chen, Qin Chen, Jie Zhou, Yishen He, Liang He
First submitted to arxiv on: 1 Mar 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 study proposes a novel benchmark called DiaHalu to evaluate the dialogue-level hallucination in large language models (LLMs). The existing benchmarks focus on sentence- or passage-level hallucinations and are not naturally generated by LLMs. DiaHalu addresses this gap by simulating authentic human-machine interaction scenarios, integrating collected topics into system prompts, and modifying contents that do not adhere to human language conventions. The benchmark covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucination. Experiments with well-known LLMs and detection methods demonstrate the challenging nature of DiaHalu. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at understanding and generating human-like text, but they can still make mistakes. One problem is that they might create fake information or conversations that sound real. To help fix this issue, scientists created a new way to test how well LLMs do in this area. This approach simulates real conversations between humans and machines, which helps researchers figure out what makes the models better or worse at avoiding these mistakes. |
Keywords
» Artificial intelligence » Hallucination
