Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 10314-10325· 0 citations· 13 references
Abstract
Large language models (LLMs) and large multimodal models (LMMs) are increasingly applied in scientific dialogue, but it remains unclear whether they can reliably ground specific dialogue statements to paper-based evidence. A central challenge is paper-grounded hallucination under a paper-as-truth setting: statements that are contradicted by, not found in, or otherwise not decidable from the paper PDF. These hallucinations can be caused by both human misinterpretations and model-generated assertions, ultimately undermining the efficiency, fairness, and credibility of scientific dialogue. Existing benchmarks often overlook this issue, focusing either on subjective macro-level quality assessments or lacking cross-modal evidence localization. We introduce ADAM-Bench (Auditing Dialogue Assertions with Multimodal Evidence), a benchmark for paper-grounded hallucinations in scientific dialogue. Starting from around 27,000 papers, ADAM-Bench is a multi-layer benchmark with three tiers: Scale, Core, and Gold. ADAM-Bench pairs approximately 1 million atomic claims with over 7 million multimodal evidence objects extracted from the corresponding PDFs. We build it through a four-stage pipeline of claim atomization, candidate evidence recall, model-assisted pre-alignment, and human verification. Based on this dataset, we define two tasks: hallucination detection and minimal evidence set localization. Additionally, to avoid the brittleness introduced by single-rationale supervision, we formalize minimal evidence as a set of equivalent evidence sets and evaluate localization by best-matching against multiple gold evidence sets. We conduct a comprehensive benchmark of 34 LLMs and 10 LMMs, spanning large proprietary models (Claude-Opus-4-6, GPT-5.2) and open-source models (Qwen3-235B, GLM-4.6V 106B). Results are markedly low (25.2%--51.1%), indicating that grounding conversational hallucinations in real multimodal papers remains far from solved. We hope this benchmark will contribute to building scientific assistants that make calibrated judgments, cite minimal, auditable evidence, and mitigate the impact of hallucinations in scientific discovery evaluation.
Recent inference-time hallucination mitigation methods for large vision-language models (LVLMs) report strong gains on hallucination benchmarks. However, it remains unclear whether lower hallucination scores reflect improved multimodal grounding or more conservative generation. We evaluate six mitigation methods across three LVLMs and four benchmarks, including hallucination-focused evaluation and the diverse capability benchmark MMStar. Our analysis reveals two consistent patterns. First, hallucination reduction is often coupled with reduced informativeness: methods that lower hallucination rates also reduce object recall, visual coverage, or response detailedness. Second, improvements on hallucination benchmarks do not reliably transfer to broader multimodal capabilities, with methods showing inconsistent or degraded performance on fine-grained perception and reasoning tasks. Our findings suggest that current evaluation protocols may overestimate progress by rewarding conservative generation. We argue that hallucination mitigation should be evaluated as a faithfulness--informativeness--capability trade-off rather than through hallucination scores alone.
Mehrdad Fazli, Sina Mansouri, Mohit Marvania et al.· 0 citations
The results suggest that no single architecture guarantees factual reliability, however, contextual grounding and verification mechanisms can significantly improve response quality and highlight the importance of combining language modelling capabilities with grounding strategies to support the development of more reliable AI systems.
HallDetect, a lightweight, reference-free, and black-box framework for hallucination detection, is presented, a lightweight, reference-free, and black-box framework for hallucination detection that is evaluated not only on summarization but across a broader range of source-grounded generation settings.
Achir Oukelmoun, N. Semmar, Gäel de Chalendar· 0 citations
This paper studies hallucinated CoT as a discourse-structural phenomenon, not only a factual one, and suggests that discourse structure provides an interpretable signal for detecting reasoning hallucinations and complements existing factuality and uncertainty-based hallucination detectors.
Superhuman game engines in domains like chess have made expert-level evaluations easily accessible, yet they communicate what is true without the natural-language explanations that make such expertise educationally useful to experts and non-experts alike. Large language models could, in principle, bridge this gap, but they frequently hallucinate due to limited domain-specific knowledge, and standard reference-based or LLM-as-a-judge frameworks cannot reliably detect these errors. In this work, we present ACT-Eval, an evaluation framework that decomposes chess commentary into atomic claims and routes them to engine-supported tools and expert-annotated gold references to assess factual correctness, conceptual coverage, and move-quality judgment. We release a benchmark of 325 position--move pairs spanning pedagogical, tournament, and critical positions, including 125 positions with expert-verified gold atoms and a five-class error taxonomy. Evaluating leading proprietary and open-weight models, we find that factual hallucinations remain pervasive in chess commentary: GPT-5.4 without tools produces incorrect sub-claims 22.0% of the time, while smaller open-weight models exceed 40%. Although tool augmentation substantially improves factual correctness and move-quality assessment, coverage of expert strategic and tactical ideas remains limited across all models. Human calibration shows that ACT-Eval's factual judgments fall within the observed range of inter-human agreement, while its coverage scores correlate strongly with human assessments of strategic completeness.
S. A. Hebbar, Peiyao Sheng, Sewoong Oh et al.· 0 citations
Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious consequences. To better understand this problem, we conduct a fine-grained analysis of hallucination behavior in eight legal RAG systems across two legal corpora, the GDPR (in English) and a national civil law (in French). Using claim-level and answer-level evaluation, we report on hallucination density and severity, analyze performance across question categories and user personas, and validate our findings on an independent set of 142 legal-expert-authored questions. Our results show that hallucinations remain pervasive, ranging from less than 10% of responses for the best-performing systems to nearly half in the worst case. We further find that false-premise questions, containing incorrect assumptions that must be rejected, produce high hallucination rates on the manually-drafted questions.
Souvick Das, S. Abualhaija, D. Bianculli· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
