Overview of the S ci H al25 Shared Task on Hallucination Detection for Scientific Content (2025)

Abstract: This paper provides an overview of the Hallucination Detection for Scientific Content (SciHal) shared task held in the 2025 ACL Scholarly Document Processing workshop. The task invites participants to detect hallucinated claims in answers to research-oriented questions generated by real-world GenAI-powered research assistants. This task is formulated as a multi-label classification problem, each instance consists of a question, an answer, an extracted claim, and supporting reference abstracts. Participants are asked to label claims under two subtasks: (1) coarse-grained detection with labels Entailment, Contradiction, or Unverifiable; and (2) fine-grained detection with a more detailed taxonomy including 8 types.The dataset consists of 500 research-oriented questions collected over one week from a generative assistant tool. These questions were rewritten using GPT-4o and manually reviewed to address potential privacy or commercial concerns. In total, 10,000 reference abstracts were retrieved, and 4,592 claims were extracted from the assistant’s answers. Each claim is annotated with hallucination labels. The dataset is divided into 3,592 training, 500 validation, and 500 test instances.Subtask 1 saw 88 submissions across 10 teams while subtask 2 saw 39 submissions across 6 teams, resulting in a total of 5 published technical reports. This paper summarizes the task design, dataset, participation, and key findings.

## introduction generative ai-powered academic research assistants are transforming how research is conducted. these systems enable users to pose research-related questions in natural language and receive structured, concise summaries supported by relevant references. however, hallucinations pose a significant challenge to fully trusting these automatically generated scientific answers. recent shared tasks have begun to address hallucination detection across domains such as biomedical summarization @xcite and scientific content @xcite . while these efforts have advanced benchmarking in specific settings, they are often limited to binary classification or constrained domains. broader benchmarks like hal-eval @xcite provide generalpurpose evaluation but lack task grounding. to fill this gap, scihal introduces a multi-label hallucination detection task grounded in real-world scientific question answering. the task invites participants to detect hallucinated claims in answers to research-oriented questions generated by a realworld genai-powered research assistant. this task is formulated as a multi-label classification problem, each instance consists of a question, an answer, an extracted claim, and supporting reference abstracts. the shared task is hosted on kaggle 1 . weighted f1 score is used as the primary evaluation metric to account for class imbalance. subtask 1 attracted 109 submissions from 11 participating teams. on the public leaderboard (validation set), the top three teams were @xcite , @xcite , and @xcite , achieving weighted f1 scores of 0.60, 0.59, and 0.59, respectively. on the private leaderboard (test set), the top three teams were @xcite , @xcite , and @xcite , with scores of 0.62, 0.60, and 0.59. subtask 2 attracted 43 submissions from 7 participating teams. on the public leaderboard (validation set), the top three teams were @xcite , @xcite , and jb, achieving weighted f1 scores of 0.51, 0.50, and 0.49. on the private leaderboard (test set), the top teams were @xcite , @xcite , le and thin (2025), jb, carla and uban (2025), achieving weighted f1 scores of 0.47, 0.47, 0.47, 0.47, 0.46. these results highlight the difficulty and complexity of the task. participating teams employed a diverse range of approaches, including fine-tuning transformer-based encoders, prompting large language models (llms), and hybrid methods using internal state representations. additionally, the subjective nature of hallucination detection, particularly in edge cases, introduces annotation challenges and potential label noise. improving annotation consistency remains an important direction for future work. ## related work recent years have seen a growing interest in shared tasks on hallucination detection in the context of text generation by llms. one of the earliest domain-specific efforts is the trec biogen task, which evaluates the factual consistency of biomedical answers and summaries, using sentence-level labels over content generated from pubmed articles @xcite . in the scientific domain, the shroom shared task @xcite introduced hallucination detection and mitigation challenges for scientific abstracts and question answering, incorporating both binary and fine-grained classifications. the shroom dataset includes human-annotated claims with hallucination labels grounded in scientific references, offering valuable insights but remaining limited in scale and question diversity. beyond biomedical and scientific settings, the hal-eval benchmark @xcite provides a multi-domain benchmark covering summarization, question answering, and data-to-text generation, annotated with fine-grained hallucination spans. although these efforts contribute valuable datasets and evaluation protocols, they often focus on either general-purpose outputs, a single domain, or a binary classification setup. in contrast, scihal is specifically designed for hallucination detection in academic research assistants. it introduces a two-tiered taxonomy (coarse-and fine-grained), grounded in real-world user queries and scientific reference abstracts, with large-scale expert annotations across five scientific domains. this makes sci-hal the first shared task to target hallucination detection in the context of retrieval-augmented question answering for scholarly research. ## hallucination taxonomy creation there is currently no established taxonomy for hallucination types specific to scientific content. our goal is to develop one that (1) reflects real-world error patterns, (2) remains manageable for human annotators, and (3) ensures high label quality. existing work has developed detailed taxonomies to characterize hallucinations in large language models (llms). early studies often framed hallucinations as a binary phenomenon, i.e. factual versus non-factual, but more recent work proposes nuanced classifications. a common distinction is between intrinsic hallucinations, which contradict the input or reference, and extrinsic hallucinations, which introduce unsupported content @xcite . other taxonomies categorize hallucinations based on the nature of the error, such as entity-level, numeric, or reasoning-based inconsistencies @xcite . some frameworks adopt a multi-dimensional view; for example, rawte et al. (2023) organize hallucinations by orientation (harmful vs. benign), grounding (intrinsic vs. extrinsic), and fine-grained types, including acronym misuse, quantitative errors, and temporal inaccuracies. these efforts provide a foundation for designing task-specific taxonomies in domains like scientific content generation @xcite . drawing from a small-scale analysis of 136 user feedback responses, we identified the most frequent error types: missing the main concept (34.6%), factually incorrect (21.3%), too general (21.3%), and unrelated references (11.8%). these findings highlight recurring issues in generative ai outputs. our final taxonomy is informed by both in-house analysis of genai-powered research assistant outputs and broader studies of hallucination patterns in general-purpose genai systems. figure 1 presents the decision tree that underpins our taxonomy, which was included in the annotation guidelines provided to subject-matter experts (smes). definitions and examples for each hallucination type are listed in ## data creation the dataset consists of claim-level annotations designed to evaluate the factual consistency between claims in generated answers and their cited references within scientific retrieval-augmented generation (rag) systems. the data are primarily derived the claim is explicitly and clearly supported by at least one passage in the reference abstracts, while not being contradicted by any other passage from the reference. reference: the weather is rainy and the wind is blowing. claim: the weather is rainy. reference: the weather is rainy and the wind is blowing. claim 1: he was born in the netherlands. → the reference addresses the weather but the claim mentions where a person was born, being unrelated so unverifiable. unver relunvef the claim and all abstracts address the same broad topic, but the specific idea presented in the claim or any of its sub-parts is not covered, making the claim unverifiable. reference: the weather is rainy and the wind is blowing. claim 1: the rainy weather is causing widespread flooding in the region. → both the reference and the claim address the weather, but nothing is mentioned about flooding. contra entierr the claim contains an erroneous entity that contradicts what is stated in the reference. a named entity is a real-world object, such as a person, location, organization, product, etc., that can be denoted with a proper name. reference: the weather is rainy, as forecasted by bbc. claim: the weather is rainy, as forecasted by the weather channel. ## analysis of label quality the following sections will present an analysis of the label distribution and quality. to this end, it is important to note that a second sme was consulted only for claims where there was disagreement between the llm judge and the first sme. as a result, the comparisons between sme 1 and sme 2 in the following analysis relate specifically to claims that are potentially more difficult to label or more subjective. these challenging claims constitute approximately 50% of the entire dataset. consequently, the observed agreement rate between the two smes may be lower than if the comparison were conducted on the entire dataset. the third sme was excluded from this analysis because they were not independent, having had access to both the labels and justifications provided by the first two smes. overall, the agreement rates (accuracy) between the two smes on the difficult part of the entire dataset are 0.55 for subtask 1 and 0.44 for subtask 2, respectively. ## discussion the scihal shared task attracted a wide range of approaches to hallucination detection in scientific content. the participating teams explored diverse techniques including prompt-based llms, finetuned encoders, hybrid fusion strategies, and internal state modeling. top-performing systems consistently relied on fine-tuning transformer models. this outcome suggests that supervised adaptation remains effective in domains with limited training data and high factual precision requirements. annotation quality remains a key challenge. despite expert annotators and adjudication, hallucination labeling involves subjectivity and is timeintensive -each claim required an average of 7 minutes to annotate. this underscores the need for more scalable and consistent annotation protocols. although fine-grained hallucination types are difficult to annotate, they are particularly valuable for real-world applications, as they reflect common failure modes observed in practical genai systems. these include, but are not limited to: nonsynonymous term substitutions, suboptimal grounding, direct copying instead of summarization, overgeneralization from a single source, tangential continuations, avoidance of direct answers, conceptual conflation, evidence overstatement. capturing these phenomena offers critical insights into model behavior. however, such cases are relatively rare, making it challenging to collect sufficient labeled instances. this rarity, combined with the nuanced nature of these errors, also poses significant challenges for future work. another limitation lies in the data split strategy. the train/val/test sets were divided by claim rather than by question, resulting in all test questions being seen during training. however, the claims in train are very different than test: less than 1.5% of test claims showed high similarity to training claims. repeated exposure to identical questions and answer contexts may still favor memorization, particularly for fine-tuned models. future iterations should ensure both question-and context-disjoint splits to better assess generalization. in future work, we aim to expand hallucination type coverage, improve annotation consistency, and adopt stricter data partitioning to enable more robust benchmarking.