Summary of Order Matters in Hallucination: Reasoning Order As Benchmark and Reflexive Prompting For Large-language-models, by Zikai Xie
Order Matters in Hallucination: Reasoning Order as Benchmark and Reflexive Prompting for Large-Language-Models
by Zikai Xie
First submitted to arxiv on: 9 Aug 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 paper explores the limitations of large language models (LLMs) and proposes a novel approach to assess their consistency. Specifically, it addresses the “hallucination problem” where LLMs generate coherent but factually inaccurate responses. The authors demonstrate that the order in which LLMs generate answers and reasoning impacts their consistency, with significant variations between two approaches. To address this issue, they introduce a new benchmark method comparing responses generated through these two approaches, effectively identifying instances of fabricated answers. Additionally, they propose a prompt strategy designed to mitigate this problem, achieving improved performance across various LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study investigates how large language models work and finds some surprising limitations. These models are often very good at generating human-like text, but sometimes they make mistakes or even invent facts that aren’t true! The researchers discovered that the way these models generate answers affects their accuracy, with big differences depending on whether they first come up with an answer and then explain why, or start by explaining and then conclude. To fix this problem, the authors created a new test to measure how well LLMs do in being consistent, and came up with a simple way to ask questions that helps them do better. |
Keywords
» Artificial intelligence » Hallucination » Prompt
