Summary of Mirror: a Multiple-perspective Self-reflection Method For Knowledge-rich Reasoning, by Hanqi Yan et al.
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
by Hanqi Yan, Qinglin Zhu, Xinyu Wang, Lin Gui, Yulan He
First submitted to arxiv on: 22 Feb 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 In this paper, researchers explore the limitations of large language models (LLMs) when dealing with knowledge-rich problems without external resources. They find that LLMs struggle to reflect on their own outputs and revisit predictions even when receiving explicit negative feedback. To address these issues, the authors propose a novel method called Mirror, which enables LLMs to reflect from multiple-perspective clues through a heuristic interaction between a Navigator and a Reasoner. The experiments demonstrate the superiority of Mirror over several contemporary self-reflection approaches, and an ablation study shows that the proposed strategies alleviate the challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models can struggle when they need to think deeply about complex problems without being able to ask for help or look up answers online. These models are good at talking and writing, but sometimes they get stuck thinking in one way and can’t change their minds even when someone tells them they’re wrong. The researchers came up with a new idea called Mirror that helps the models think more deeply and consider different perspectives without getting stuck. They tested it on several problems and found that it worked better than other methods. |
