Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection

TL;DR

This paper presents Reinforced Unanswerability Learning (RUL), a hybrid training method that improves large language models' ability to detect unanswerable questions and generate trustworthy responses, enhancing AI reliability and user trust.

Contribution

Introduction of RUL, a novel hybrid training paradigm combining hierarchical unanswerability detection with reinforcement learning and a new annotated dataset, improving LLM trustworthiness.

Findings

RUL significantly improves unanswerability detection accuracy.

RUL increases appropriate refusal responses for unanswerable questions.

Human evaluations show enhanced helpfulness and trustworthiness.

Abstract

The pervasive deployment of large language models (LLMs) in conversational AI systems has revolutionized information access, yet their propensity for generating factually unsupported or hallucinated responses remains a critical impediment to trustworthiness and widespread adoption. This paper introduces Reinforced Unanswerability Learning (RUL), a novel hybrid training paradigm designed to imbue LLMs with the intrinsic capability to accurately detect unanswerable questions and generate reliably appropriate responses. Unlike conventional approaches that rely on external classifiers or simple prompting, RUL integrates a discriminative unanswerability prediction head with the LLM's generative core, guided by a multi-stage learning strategy. This includes supervised fine-tuning on a novel, richly annotated dataset, Enhanced-CAsT-Answerability (ECA), which features hierarchical answerability labels and ground-truth refusal responses. Crucially, RUL incorporates a subsequent reinforcement learning with human feedback (RLHF) phase to refine the nuance, helpfulness, and informativeness of refusal responses. Extensive experiments demonstrate RUL's superior performance, achieving significantly higher accuracy in unanswerability detection across sentence, paragraph, and ranking levels, and substantially increasing the generation of appropriate refusals for unanswerable queries, alongside strong performance on answerable questions. Human evaluations further corroborate RUL's effectiveness, highlighting a marked improvement in perceived helpfulness and trustworthiness, ultimately paving the way for more reliable and user-centric conversational AI.