Implicit Probabilistic Reasoning Does Not Reflect Explicit Answers in Large Language Models

TL;DR

This paper compares explicit and implicit probabilistic reasoning in large language models, revealing that models perform well on explicit tasks but often diverge from true probabilities during implicit reasoning in text generation.

Contribution

The paper introduces an alternative implicit probabilistic reasoning evaluation method, highlighting discrepancies between models' explicit answers and their implicit reasoning in text generation.

Findings

Models perform well on explicit probabilistic reasoning (MCQs)

Implicit reasoning predictions often diverge from true probabilities

Implicit reasoning is influenced by prior events and background info

Abstract

The handling of probabilities in the form of uncertainty or partial information is an essential task for LLMs in many settings and applications. A common approach to evaluate an LLM's probabilistic reasoning capabilities is to assess its ability to answer questions pertaining to probability through the use of multiple-choice questions (MCQs). However, this paradigm, which we refer to as explicit probabilistic reasoning, has been shown in the literature to yield significant limitations (e.g., sensitivity to answer ordering). In this work, we introduce an alternative approach, named implicit probabilistic reasoning, which evaluates the models' ability to integrate probabilistic reasoning into their text generation process. To achieve this, we rephrase MCQs as text-completion scenarios with a determined set of outcomes and compare the model's next-token probability assignments to the true likelihood of the outcomes. In line with previous work, we find that models exhibit solid performance in their explicit probabilistic reasoning (i.e., answers to MCQs). However, during text completion (i.e., implicit probabilistic reasoning), where the same information must be taken into account to generate text, the models' predictions often significantly diverge from the known ground truth. For instance, our evaluation method reveals that implicit probabilistic reasoning is improperly influenced by many factors, such as independent prior events, partial observations about a result, or statistical background information. All of these issues can cause erroneous results to be produced in text generation, which are not detected by conventional MCQ-based evaluation.