DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning (2410.01772v1)

Abstract: LLMs are ideal for decision-making due to their ability to reason over long contexts and identify critical factors. However, challenges arise when processing transcripts of spoken speech describing complex scenarios. These transcripts often contain ungrammatical or incomplete sentences, repetitions, hedging, and vagueness. For example, during a company's earnings call, an executive might project a positive revenue outlook to reassure investors, despite significant uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a new framework that constructs probabilistic factor profiles from complex scenarios. DeFine then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in novel situations. Our framework separates the tasks of quantifying uncertainty in complex scenarios and incorporating it into LLM decision-making. This approach is particularly useful in fields such as medical consultations, negotiations, and political debates, where making decisions under uncertainty is vital.

• The paper introduces a novel framework that integrates probabilistic factor profiles with analogical reasoning to improve LLM decision-making under uncertainty.
• It employs Bayesian principles and the Bradley-Terry model to quantify factor importance and retrieve historical analogs for high-stakes decisions.
• Experimental results show nearly a 7% improvement in both accuracy and macro-averaged F-score, demonstrating its potential across finance, medicine, and legal domains.

Enhancing Decision-Making in LLMs Through DeFine: A Structured and Analogical Approach

"DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning" presents a compelling framework that addresses the challenges faced by LLMs in decision-making tasks, particularly when dealing with complex, real-world scenarios such as financial investment decisions. The framework introduces a novel mechanism combining probabilistic factor profiles and analogical reasoning to improve the efficacy and reliability of LLMs under uncertainty. This paper advances the understanding of how LLMs can collaborate with domain experts in fields where decisions involve high stakes, such as finance, medicine, and politics.

Key Contributions

1. Probabilistic Factor Profiles: DeFine introduces probabilistic factor profiles, which allow LLMs to aggregate information from lengthy transcripts, such as earnings calls, into a set of quantified factors with associated probabilities. These profiles systematically capture uncertainty and essential context, differentiating between outcomes that are explicitly stated and those that are unknown or ambiguous.
2. Analogical Reasoning: The framework employs analogical reasoning to draw connections between current decision scenarios and past experiences. Rather than relying on mere text matching, the use of factor profiles enables retrieval of analogous historical cases, thus enriching decision-making with contextual insights drawn from prior, similar scenarios.
3. Bayesian Decision-Making: DeFine leverages Bayesian principles, integrating the probabilistic profiles and analogical reasoning into a coherent decision-making process. This hybrid framework quantifies uncertainty and guides LLMs towards rational, data-driven decisions aligned with observed historical patterns and domain-specific heuristics.
4. Sectoral and Cross-Sectoral Influence Analysis: By using the Bradley-Terry model, the paper provides a comprehensive methodology to assess the relative importance of different factors and their outcomes within and across industry sectors. This aids in optimizing decisions based on sector-specific dynamics and cross-sectoral historical performance.

Experimental Findings

The experiments demonstrate that DeFine significantly outperforms baseline models, including decision theory-based approaches, by nearly 7% in accuracy and macro-averaged F-score. Key insights illustrate that decisions driven by meticulous factor profiling and analogical reasoning capture a wider spectrum of historical and contextual nuances, thereby improving prediction fidelity. The integration of structured summaries with inherent uncertainty measures enhances LLMs' ability to navigate complex datasets like earnings call transcripts that are often fraught with optimistic bias.

Implications and Future Work

DeFine's methodology underlines the potential of LLMs in complex decision-making tasks by offering a structured way to incorporate uncertainty and historical context, drawing parallels to human-like reasoning processes. Its implications extend beyond finance, offering a blueprint for applications in medical diagnostics and legal analysis where uncertainty and analogue reasoning play a critical role.

Future work could explore refining the analogical reasoning component further, potentially leveraging deeper neural architectures to enhance contextual matching. Exploring the integration of additional data modalities—such as sentiment analysis from stock price trends or media reports—could also refine the factor profiles and improve decision accuracy. As LLMs continue to evolve, frameworks like DeFine that augment decision-making capabilities will be vital in unlocking broader applications across multiple domains.

In conclusion, DeFine represents a robust advancement in the domain of LLM-based decision-making frameworks, setting a precedent for utilizing structured reasoning approaches to tackle the complexities inherent in real-world scenarios with significant economic and societal impacts.