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Eric Zhong
Computer Science, USC, CA, USA
Computer Science & IT

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Evidence-Calibrated RAG for Unanswerable Question Answering: Retrieval Coverage, Abstention Calibration, and Hallucination-Proxy Analysis on SQuAD 2.0 Ziliang Samuel Zhong; Jing Chen; Eric Zhong; Xinzhuo Sun
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.536

Abstract

This paper presents a controlled and reproducible empirical study of evidence-calibrated retrieval-augmented question answering (RAG) for answerable and unanswerable reading-comprehension tasks using the SQuAD 2.0 benchmark. The study focuses on whether a system should abstain when retrieved evidence is insufficient rather than always producing an answer. Six lightweight architectures were evaluated on the full validation set of 11,873 questions, including closed-book, BM25, dense, hybrid, reranked, and a proposed evidence-calibrated hybrid RAG model. The proposed approach combines hybrid top-25 retrieval, lexical reranking, deterministic extractive answering, and evidence sufficiency calibration trained on 43,482 examples. On the validation set, it achieved 31.65% exact match, 34.74% F1, 53.01% answerability accuracy, 53.71% refusal F1, and a 37.49% hallucination-proxy rate. Although overall QA performance remains modest, calibrated evidence sufficiency substantially reduced unsupported answers compared with a forced-answer hybrid reranker, lowering the hallucination-proxy rate from 77.80% while improving F1. However, evidence calibration itself remained weak (AUROC 0.5475, ECE 0.1144). The findings demonstrate that retrieval coverage alone is insufficient to prevent hallucinations and highlight the need for stronger evidence calibration in trustworthy RAG systems.
Co-Authors Jing Chen Xinzhuo Sun Ziliang Samuel Zhong