Summary of Reasoning Beyond Bias: a Study on Counterfactual Prompting and Chain Of Thought Reasoning, by Kyle Moore et al.
Reasoning Beyond Bias: A Study on Counterfactual Prompting and Chain of Thought Reasoning
by Kyle Moore, Jesse Roberts, Thao Pham, Douglas Fisher
First submitted to arxiv on: 16 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 investigates the biases present in language models trained on Massive Multi-Task Language Understanding (MMLU) data, which can lead to predictions driven by statistical patterns rather than semantic relevance. The study reveals that differences in learned regularities across answer options are predictive of model preferences and mirror human test-taking strategies. To address this bias issue, the authors introduce two novel methods: Counterfactual Prompting with Chain of Thought (CoT) and Agnostically Primed CoT (APriCoT). While Counterfactual Prompting with CoT alone is insufficient to mitigate bias, the Primed Counterfactual Prompting with CoT approach effectively reduces base-rate probability influence while improving overall accuracy. The findings suggest that mitigating bias requires a “System-2” like process and that CoT reasoning can be susceptible to confirmation bias under certain prompting methodologies. These contributions offer practical solutions for developing more robust and fair language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how language models learn from data and make predictions. It shows that these models can pick up biases in the data they were trained on, which can affect what answers they choose. The study found that these models mirror human test-taking strategies when it comes to choosing answers. To fix this problem, the researchers came up with two new ways to prompt language models: Counterfactual Prompting with Chain of Thought (CoT) and Agnostically Primed CoT (APriCoT). These methods can help make language models more fair and accurate. |
Keywords
» Artificial intelligence » Language understanding » Multi task » Probability » Prompt » Prompting
