Let's Think Var-by-Var: Large Language Models Enable Ad Hoc Probabilistic Reasoning

• URL: http://arxiv.org/abs/2412.02081v1
• Date: Tue, 03 Dec 2024 01:53:06 GMT
• Title: Let's Think Var-by-Var: Large Language Models Enable Ad Hoc Probabilistic Reasoning
• Authors: Shepard Xia, Brian Lu, Jason Eisner,
• Abstract summary: We propose to extract common sense from large language models (LLMs)<n>We focus our investigation on $textitguesstimation$ questions such as "How much are Airbnb listings in Newark, NJ?"<n>Our framework answers such a question by an $textitad hoc$ probabilistic model.
• Score: 15.568698101627088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A hallmark of intelligence is the ability to flesh out underspecified situations using "common sense." We propose to extract that common sense from large language models (LLMs), in a form that can feed into probabilistic inference. We focus our investigation on $\textit{guesstimation}$ questions such as "How much are Airbnb listings in Newark, NJ?" Formulating a sensible answer without access to data requires drawing on, and integrating, bits of common knowledge about how $\texttt{Price}$ and $\texttt{Location}$ may relate to other variables, such as $\texttt{Property Type}$. Our framework answers such a question by synthesizing an $\textit{ad hoc}$ probabilistic model. First we prompt an LLM to propose a set of random variables relevant to the question, followed by moment constraints on their joint distribution. We then optimize the joint distribution $p$ within a log-linear family to maximize the overall constraint satisfaction. Our experiments show that LLMs can successfully be prompted to propose reasonable variables, and while the proposed numerical constraints can be noisy, jointly optimizing for their satisfaction reconciles them. When evaluated on probabilistic questions derived from three real-world tabular datasets, we find that our framework performs comparably to a direct prompting baseline in terms of total variation distance from the dataset distribution, and is similarly robust to noise.

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