BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models

• URL: http://arxiv.org/abs/2404.12494v2
• Date: Wed, 16 Oct 2024 17:45:10 GMT
• Title: BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models
• Authors: Yu Feng, Ben Zhou, Weidong Lin, Dan Roth,
• Abstract summary: Predictive models often need to work with incomplete information in real-world tasks. Current large language models (LLM) are insufficient for such accurate estimations. We propose BIRD, a novel probabilistic inference framework.
• Score: 52.46248487458641
• License:
• Abstract: Predictive models often need to work with incomplete information in real-world tasks. Consequently, they must provide reliable probability or confidence estimation, especially in large-scale decision making and planning tasks. Current large language models (LLM) are insufficient for such accurate estimations, but they can generate relevant factors that may affect the probabilities, produce coarse-grained probabilities when the information is more complete, and help determine which factors are relevant to specific downstream contexts. In this paper, we make use of these capabilities of LLMs to provide a significantly more accurate probabilistic estimation. We propose BIRD, a novel probabilistic inference framework that aligns a Bayesian network with LLM abductions and then estimates more accurate probabilities in a deduction step. We show BIRD provides reliable probability estimations that are 30\% better than those provided directly by LLM baselines. These estimates can further contribute to better and more trustworthy decision-making.

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