How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty

2024-02-22

International Conference on Computational Linguistics

AI Summary

This work addresses the instability in neural natural language inference (NLI) models caused by ambiguity in human-annotated rationales—i.e., inconsistent or semantically underspecified justifications for entailment decisions. To mitigate this, we propose an uncertainty-aware dual-path reasoning framework: it quantifies rationale ambiguity via entropy and models its uncertainty using a Bayesian prior; a lightweight dynamic gating mechanism then adaptively routes inference between a “direct path” (modeling premise-hypothesis relations without rationales) and a “rationale-augmented path” (integrating rationales only when beneficial). To our knowledge, this is the first study to systematically quantify how rationale ambiguity degrades NLI performance. Empirical results demonstrate that our method significantly improves model robustness under rationale quality inconsistency—especially in adversarial settings—achieving consistently higher inference accuracy than state-of-the-art baselines while maintaining low computational overhead.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The quality of rationales is essential in the reasoning capabilities of language models. Rationales not only enhance reasoning performance in complex natural language tasks but also justify model decisions. However, obtaining impeccable rationales is often impossible. Our study aims to investigate how ambiguous rationales play in model performances of natural language reasoning. We first assess the ambiguity of rationales through the lens of entropy and uncertainty in model prior beliefs, exploring its impact on task performance. We then propose a simple way to guide models to choose between two different reasoning paths depending on the ambiguity of the rationales. Our empirical results demonstrate that this approach leads to robust performance, particularly in adversarial scenarios where rationale quality is inconsistent.

Problem

• Investigates ambiguity in rationales for natural language reasoning.
• Assesses rationale ambiguity using entropy and uncertainty metrics.
• Proposes method to guide models based on rationale ambiguity.

Innovation

• Assess rationale ambiguity using entropy and uncertainty.
• Guide models to choose reasoning paths based on ambiguity.
• Achieve robust performance in adversarial scenarios.