Summary of Self-contrast: Better Reflection Through Inconsistent Solving Perspectives, by Wenqi Zhang et al.
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
by Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, Weiming Lu
First submitted to arxiv on: 4 Jan 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 Large Language Models (LLMs) have gained attention for their reflection capabilities, but recent research suggests that without external feedback, their intrinsic reflection is unstable. A key bottleneck is the quality of self-evaluated feedback, which often exhibits overconfidence or high randomness, leading to stubborn and inconsistent feedback. To remedy this, we propose Self-Contrast, a method that adapts diverse solving perspectives tailored to the request, contrasts differences, and summarizes discrepancies into a checklist for re-examination and elimination. This approach endows LLMs with diverse perspectives to alleviate biases and highlights potential errors or uncertainties that LLMs may overlook. Our strategy was tested on various reasoning and translation tasks using different LLMs, demonstrating its effectiveness and generality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can learn from themselves. But did you know that they often get stuck in their own thinking? This is because they don’t have a way to check if what they’re saying makes sense or not. Our research shows that this problem comes from the computer’s own feedback being too biased or random, which means it’s hard for them to correct themselves. We came up with an idea called Self-Contrast that helps computers look at different ways of solving problems and compare them. This makes it easier for them to catch mistakes and be more accurate. We tested this idea on several tasks and found that it worked really well! |
Keywords
» Artificial intelligence » Attention » Translation
