Summary of How Ambiguous Are the Rationales For Natural Language Reasoning? a Simple Approach to Handling Rationale Uncertainty, by Hazel H. Kim
How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty
by Hazel H. Kim
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 investigates how ambiguous rationales affect language model performances in natural language reasoning tasks. Researchers aim to understand the impact of ambiguous rationales on task performance and propose a simple approach to guide models in choosing between two reasoning paths based on rationale ambiguity. The proposed method demonstrates robust performance, particularly in adversarial scenarios where rationale quality is inconsistent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how language models make decisions when they don’t have perfect reasons for doing so. This happens often in complex language tasks. Researchers want to know if these “bad” reasons affect the model’s overall performance. They came up with a way to help the model decide which path to take based on how unclear its reasons are. It turns out that this approach works really well, especially when the reasons for making decisions get even worse. |
Keywords
* Artificial intelligence * Language model
