Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong
📰 ArXiv cs.AI
LLMs become more self-confident in their incorrect answers when using reasoning for multiple choice questions, highlighting the need for careful evaluation of model confidence
Action Steps
- Evaluate LLM performance on multiple choice questions using both direct answering and reasoning approaches
- Compare the confidence levels of LLMs when answering correctly versus incorrectly
- Analyze how reasoning affects LLM confidence in incorrect answers
- Develop strategies to mitigate overconfidence in LLMs, such as calibration techniques or uncertainty estimation
- Test the effectiveness of these strategies on various multiple choice question datasets
Who Needs to Know This
NLP researchers and developers working with LLMs can benefit from understanding how reasoning affects model confidence, especially when evaluating model performance on multiple choice questions
Key Insight
💡 Reasoning can increase LLM confidence, even when the model is wrong, highlighting the need for careful evaluation and mitigation strategies
Share This
🚨 LLMs can become overconfident in incorrect answers when using reasoning for multiple choice questions 🚨
Key Takeaways
LLMs become more self-confident in their incorrect answers when using reasoning for multiple choice questions, highlighting the need for careful evaluation of model confidence
Full Article
Title: Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong
Abstract:
arXiv:2501.09775v3 Announce Type: replace-cross Abstract: Multiple Choice Question (MCQ) tests are among the most used methods for evaluating large language models (LLMs). Besides checking the correctness of the selected answer, evaluations often consider the model's confidence through the probability assigned to its response. In this work, we investigate how LLM confidence is influenced by the answering approach when the model answers directly or reasons before responding. Experiments on a gene
Abstract:
arXiv:2501.09775v3 Announce Type: replace-cross Abstract: Multiple Choice Question (MCQ) tests are among the most used methods for evaluating large language models (LLMs). Besides checking the correctness of the selected answer, evaluations often consider the model's confidence through the probability assigned to its response. In this work, we investigate how LLM confidence is influenced by the answering approach when the model answers directly or reasons before responding. Experiments on a gene
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