Summary of Llm Hallucination Reasoning with Zero-shot Knowledge Test, by Seongmin Lee et al.
LLM Hallucination Reasoning with Zero-shot Knowledge Test
by Seongmin Lee, Hsiang Hsu, Chun-Fu Chen
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The paper addresses the issue of LLM hallucination, where language models (LLMs) generate unfaithful text. Existing methods to detect this phenomenon rely on external knowledge, fine-tuning LLMs, or labeled datasets and fail to distinguish between different types of hallucinations. The authors introduce a new task called Hallucination Reasoning, which classifies LLM-generated text into aligned, misaligned, or fabricated categories. A zero-shot method is proposed that assesses whether an LLM has sufficient knowledge about a given prompt and text. Experimental results on new datasets demonstrate the effectiveness of this approach in hallucination reasoning and emphasize its importance for improving detection performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with language models (LLMs) called “hallucination.” Sometimes, LLMs make up false information instead of providing accurate text. To fix this issue, researchers are working on ways to detect when an LLM is making something up. The authors propose a new approach that can identify three types of hallucinations: when the model gets it right, when it’s close but not quite correct, and when it completely makes things up. They tested their method on new data sets and found it works well for identifying these different kinds of hallucinations. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Prompt » Zero shot
