# Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection

arXiv: https://arxiv.org/abs/2506.01104 · HTML: https://mlsanity.com/paper/2506.01104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners](https://mlsanity.com/paper/2205.10747/) (2022)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval](https://mlsanity.com/paper/2010.11915/) (2020)
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- [Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion](https://mlsanity.com/paper/2501.04606/) (2025)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment](https://mlsanity.com/paper/2310.08372/) (2023)
- [InstructPatentGPT: Training patent language models to follow instructions with human feedback](https://mlsanity.com/paper/2406.16897/) (2024)
- [Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?](https://mlsanity.com/paper/2410.21529/) (2024)
- [Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2401.11452/) (2024)

## Abstract (arXiv)

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.