Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths (2412.08281v1)

Abstract: LLMs are increasingly used to build agents to perform more complex tasks. As LLMs perform more complicated reasoning through longer interactions, self-consistency, i.e., the idea that the answer obtained from sampling and marginalising a number of multiple independent inferences is more likely to be correct, has received much attention as a simple validation technique. This paper aims to empirically verify this intuitive hypothesis by predicting the correctness of answers obtained using self-consistency from properties of the samples of reasoning paths. We introduce Lachesis, a predictive model for self-consistency based LLM inferences, and empirically evaluate it using AutoFL, a recently proposed LLM-based fault localisation technique, as the target technique that uses self-consistency. Lachesis converts collected reasoning paths from AutoFL using specifically designed reasoning path representations, and trains LSTM and GCN models to predict whether a given set of reasoning paths would result in a correct answer. The results suggest that Lachesis can predict the correctness of answers with a precision of up to 0.8136, highlighting the possibility of training a predictive model that can allow early termination of inferences that are not likely to be successful.

• The paper introduces a novel method that uses structural properties of reasoning paths to predict the correctness of LLM inferences.
• It employs LIM and LIG representations with LSTM and GCN models to achieve up to 0.8136 precision in fault localization tasks.
• This approach has the potential to reduce computational costs by enabling early termination of non-promising inference paths.

Analysis of "Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths"

The paper "Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths" introduces a novel approach to enhancing the efficiency of LLMs in reasoning tasks through predictive analysis. The authors propose Lachesis, a model grounded in the hypothesis that the structural properties of reasoning paths can predict the correctness of self-consistency-based LLM inferences.

Key Concepts and Methodology

LLMs have become pivotal in performing semantic reasoning across diverse domains, yet their computational resource demands necessitate efficient operational frameworks. This paper focuses on self-consistency, a concept where the correctness of an LLM inference is indicated by the convergence of multiple, independent reasoning paths to a consistent answer. The aim is to assess whether these reasoning paths can foretell the correctness of the conclusions derived through LLM-based tools like AutoFL.

AutoFL is leveraged as a case paper, being a fault localization technique that requires extensive contextual information about the code being analyzed. The paper proposes two primary representations for reasoning paths: LLM Inference Matrix (LIM) and LLM Inference Graph (LIG), which capture these paths in matrix and graph forms, respectively. These representations are then input into Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models to predict the correctness of the answers generated by the LLM.

Numerical Results

Lachesis, when evaluated alongside AutoFL, demonstrated its capability to predict the correctness of the fault localization process with a precision of up to 0.8136. This precision underscores the potential of using learning models to anticipate the accuracy of LLM outputs. The inclusion of information about function types, arguments, and answers in the representation notably enhances predictive performance, indicating the importance of rich feature sets.

Implications and Future Directions

The findings from Lachesis have both theoretical and practical implications for the field of AI. Theoretically, they contribute to a deeper understanding of self-consistency and how it can be exploited beyond conventional majority-vote principles. Practically, Lachesis shows promise in mitigating the computational and environmental costs associated with LLM usage by potentially enabling early termination of non-promising inference paths.

Moving forward, research could explore the integration of Lachesis into broader AI applications, refining its ability to generalize across different LLM-based tools and tasks. Moreover, investigations into alternative representations and predictive models could yield further improvements in accuracy and computational efficiency. The paper lays a foundation for these explorations, emphasizing the balance of precision and recall in constructing robust predictive systems.

In conclusion, the paper by Kim et al. presents a significant step in optimizing LLM performance through predictive assessment of reasoning paths. While the approach shows great promise, future research can expand on these findings to refine the efficiency and applicability of predictive models in AI-driven reasoning tasks.