Summary of Can Language Models Explain Their Own Classification Behavior?, by Dane Sherburn et al.
Can Language Models Explain Their Own Classification Behavior?
by Dane Sherburn, Bilal Chughtai, Owain Evans
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 investigates whether large language models (LLMs) can provide faithful high-level explanations of their internal processes. The study introduces a dataset called ArticulateRules, comprising few-shot text-based classification tasks generated by simple rules, each accompanied by a natural-language explanation. The authors test whether LLMs that have learned to classify inputs accurately (both in- and out-of-distribution) can articulate freeform natural language explanations matching their classification behavior. The paper evaluates various LLMs, including GPT-3 and GPT-4, showing significant variations in articulation accuracy between models. Additionally, the authors investigate methods to improve GPT-3’s articulation accuracy. The study releases the ArticulateRules dataset for testing self-explanation in LLMs trained in-context or through finetuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores whether big language models can explain how they make decisions. The scientists created a special dataset with simple rules and accompanying explanations to test if these models can give good reasons for their actions. They found that different models are better at explaining themselves than others, but even the best models sometimes struggle to give clear explanations. The study aims to improve our understanding of how language models work and how they make decisions. |
Keywords
» Artificial intelligence » Classification » Few shot » Gpt
