CSCE: Boosting LLM Reasoning by Simultaneous Enhancing of Casual Significance and Consistency

2024-09-20

arXiv.org

AI Summary

Large language models (LLMs) suffer from causal hallucinations in long-horizon reasoning—distorted causal relationships between reasoning steps and state transitions—under chain-of-thought (CoT) paradigms, degrading both logical consistency and causal significance. Method: We propose CSCE, a non-chain reasoning framework that abandons stepwise generation in favor of end-to-end single-step output of the full reasoning trace. Crucially, CSCE innovatively incorporates treatment effect estimation—a causal inference technique—into the loss function to jointly optimize for causal significance and logical consistency. Results: Experiments demonstrate that CSCE significantly improves task success rates and reasoning efficiency across diverse reasoning benchmarks. It constitutes the first causal-aware, non-chain structured reasoning model for LLMs, establishing a novel paradigm for reasoning architecture design. By unifying causal learning with sequence-level reasoning synthesis, CSCE advances the theoretical and practical foundations of trustworthy LLM inference.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Chain-based reasoning methods like chain of thought (CoT) play a rising role in solving reasoning tasks for large language models (LLMs). However, the causal illusions between extit{a step of reasoning} and extit{corresponding state transitions} are becoming a significant obstacle to advancing LLMs' reasoning capabilities, especially in long-range reasoning tasks. This paper proposes a non-chain-based reasoning framework for simultaneous consideration of causal significance and consistency, i.e., the Causal Significance and Consistency Enhancer (CSCE). We customize LLM's loss function utilizing treatment effect assessments to enhance its reasoning ability from two aspects: causal significance and consistency. This ensures that the model captures essential causal relationships and maintains robust and consistent performance across various scenarios. Additionally, we transform the reasoning process from the cascading multiple one-step reasoning commonly used in Chain-Based methods, like CoT, to a causal-enhanced method that outputs the entire reasoning process in one go, further improving the model's reasoning efficiency. Extensive experiments show that our method improves both the reasoning success rate and speed. These improvements further demonstrate that non-chain-based methods can also aid LLMs in completing reasoning tasks.

Problem

• Enhance LLM reasoning by improving causal significance and consistency.
• Overcome causal illusions in long-range reasoning tasks for LLMs.
• Transform reasoning process to improve efficiency and success rate.

Innovation

• Non-chain-based reasoning framework CSCE introduced
• Custom loss function enhances causal significance and consistency
• Causal-enhanced method improves reasoning efficiency and success rate