Generating Diverse Hypotheses for Inductive Reasoning

2024-12-18

arXiv.org

AI Summary

This work addresses three key limitations of large language models (LLMs) in inductive reasoning: insufficient hypothesis diversity, severe semantic redundancy, and quality degradation induced by independent and identically distributed (IID) sampling. To this end, we propose Mix-of-Concepts (MoC), a novel paradigm that decouples the conceptual space, applies controlled decoding, and incorporates temperature-adaptive analysis—thereby overcoming the text degeneration bottleneck inherent in conventional temperature-based sampling while preserving generation accuracy. MoC significantly enhances semantic diversity of generated hypotheses (+40% or more), improves reasoning accuracy, and reduces computational overhead. Empirical evaluation across multiple inductive reasoning benchmarks demonstrates that MoC consistently outperforms both standard IID sampling and existing diversity-enhancement methods. Overall, MoC establishes an efficient, controllable, and interpretable pathway for LLM-driven inductive reasoning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations. However, due to the IID sampling, semantically redundant hypotheses are frequently generated, leading to significant wastage of compute. In this paper, we 1) demonstrate that increasing the temperature to enhance the diversity is limited due to text degeneration issue, and 2) propose a novel method to improve the diversity while maintaining text quality. We first analyze the effect of increasing the temperature parameter, which is regarded as the LLM's diversity control, on IID hypotheses. Our analysis shows that as temperature rises, diversity and accuracy of hypotheses increase up to a certain point, but this trend saturates due to text degeneration. To generate hypotheses that are more semantically diverse and of higher quality, we propose a novel approach inspired by human inductive reasoning, which we call Mixture of Concepts (MoC). When applied to several inductive reasoning benchmarks, MoC demonstrated significant performance improvements compared to standard IID sampling and other approaches.

Problem

• Enhancing hypothesis diversity in LLMs
• Overcoming text degeneration in inductive reasoning
• Improving quality and diversity of generated hypotheses

Innovation

• Mixture of Concepts method
• Enhances hypothesis diversity
• Maintains text quality