Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used in high-stakes areas such as healthcare, law, and education. |
| Approach: | They propose a concept of Confidence-Probability Alignment that connects an LLM’s internal confidence to the confidence conveyed in the model’s response when explicitly asked about its certainty. |
| Outcome: | The proposed model shows the strongest confidence-probability alignment across a wide range of tasks. |
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| Challenge: | Large Language Models (LLMs) have remarkable capabilities across various domains . Reinforcement Learning with Human Feedback (RLHF) phase is crucial for training . label smoothing is a technique that replaces hard labels with soft labels . |
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| Challenge: | Existing methods for confidence estimation are primarily designed for factual QA tasks and fail to generalize to reasoning tasks. |
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, Christopher Manning
| Challenge: | Recent studies have shown that unsupervised pre-training produces large language models whose conditional probabilities are remarkably well-calibrated. |
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| Challenge: | Despite their unprecedented capabilities, large language models (LLMs) often output erroneous information, which may lead users to overly rely on their false output. |
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