ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations

• URL: http://arxiv.org/abs/2509.25868v2
• Date: Wed, 01 Oct 2025 04:57:04 GMT
• Title: ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations
• Abstract summary: Large Language Models (LLMs) frequently confabulate scientific facts, severely undermining their trustworthiness.<n>We introduce ReFACT, a benchmark of 1,001 expert-annotated question-answer pairs spanning diverse scientific domains.<n>Each instance includes both a scientifically correct answer and a non-factual counterpart annotated with precise error spans and error types.
• Score: 14.392598503431321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) frequently confabulate scientific facts, severely undermining their trustworthiness. Addressing this challenge requires benchmarks that go beyond binary factuality and enable fine-grained evaluation. We introduce ReFACT (Reddit False And Correct Texts), a benchmark of 1,001 expert-annotated question-answer pairs spanning diverse scientific domains for the detection of scientific confabulation. Each instance includes both a scientifically correct answer and a non-factual counterpart annotated with precise error spans and error types. ReFACT enables multi-stage evaluation: (1) confabulation detection, (2) fine-grained error localization, and (3) correction. We benchmark 9 state-of-the-art LLMs, revealing limited performance (about 50 percent accuracy). Even top models such as GPT-4o fail to distinguish factual from confabulated scientific answers, raising concerns about the reliability of LLM-as-judge evaluation paradigms. Our findings highlight the need for fine-grained, human-validated benchmarks to detect and correct scientific confabulation in domain-specific contexts. The dataset is available at: https://github.com/ddz5431/ReFACT

Related papers

• NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment [54.4265627247104]
  Large language models (LLMs) are increasingly used in peer review at major AI conferences.<n>Existing benchmarks assess novelty as a single holistic score.<n>We present NovGauge, a human-anchored benchmark for fine-grained novelty assessment diagnosis.
  arXiv  Detail & Related papers  (2026-09-10T08:34:56Z)
• Finetuning with Scientific Data Increases Hallucinations: A Multi-domain Factuality Evaluation of LLMs [8.700507644617964]
  Large language models (LLMs) are increasingly used to communicate and explain scientific concepts.<n>We evaluate 18 LLMs by comparing each scientifically fine-tuned model against its general-purpose base.
  arXiv  Detail & Related papers  (2026-06-19T12:03:02Z)
• SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models [63.357710580101894]
  Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice.<n>We propose a new framework named SciCustom to address the problem.<n>It enables the custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
  arXiv  Detail & Related papers  (2026-05-19T04:41:43Z)
• SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science [3.9311288356229057]
  Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants.<n>We introduce SCICONVBENCH, a benchmark for multi- turn clarification in scientific task formulation.<n>Our benchmark pairs a structured task with a rubric-based evaluation framework.
  arXiv  Detail & Related papers  (2026-05-18T16:34:45Z)
• CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era [51.63024682584688]
  Large language models (LLMs) introduce a new risk: fabricated references that appear plausible but correspond to no real publications.<n>We present the first comprehensive benchmark and detection framework for hallucinated citations in scientific writing.<n>Our framework significantly outperforms prior methods in both accuracy and interpretability.
  arXiv  Detail & Related papers  (2026-02-26T19:17:39Z)
• Knowing When Not to Answer: Abstention-Aware Scientific Reasoning [2.680633756465714]
  In scientific settings, unsupported or uncertain conclusions can be more harmful than abstaining.<n>We study this problem through an abstention-aware verification framework.<n>We evaluate this framework across two scientific benchmarks: SciFact and PubMedQA.
  arXiv  Detail & Related papers  (2026-02-15T15:29:43Z)
• SciIF: Benchmarking Scientific Instruction Following Towards Rigorous Scientific Intelligence [60.202862987441684]
  We introduce scientific instruction following: the capability to solve problems while strictly adhering to the constraints that establish scientific validity.<n>Specifically, we introduce SciIF, a multi-discipline benchmark that evaluates this capability by pairing university-level problems with a fixed catalog of constraints.<n>By measuring both solution correctness and multi-constraint adherence, SciIF enables finegrained diagnosis of compositional reasoning failures.
  arXiv  Detail & Related papers  (2026-01-08T09:45:58Z)
• FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers [10.04850395402571]
  The identification and localization of errors is a core task in peer review.<n>Recent advances in Large Language Models (LLMs) have sparked interest in their potential to support such evaluation tasks.<n>Despite the growing use of LLMs in review systems, their capabilities to pinpoint errors remain underexplored.
  arXiv  Detail & Related papers  (2025-11-26T19:19:44Z)
• Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs [16.173245551933178]
  Context-grounded hallucinations are cases where model outputs contain information not verifiable against the source text.<n>We study the applicability of LLMs for localizing such hallucinations, as a more practical alternative to existing complex evaluation pipelines.
  arXiv  Detail & Related papers  (2025-09-26T17:03:24Z)
• Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking [11.300523252168327]
  Evtextsuperscript2R combines the strengths of reference-based evaluation and verdict-level proxy scoring.<n>Evtextsuperscript2R consistently outperforms existing scoring approaches in accuracy and robustness.
  arXiv  Detail & Related papers  (2024-11-08T07:05:06Z)
• Attribution in Scientific Literature: New Benchmark and Methods [41.64918533152914]
  Large language models (LLMs) present a promising yet challenging frontier for automated source citation in scientific communication.<n>We introduce REASONS, a novel dataset with sentence-level annotations across 12 scientific domains from arXiv.<n>We conduct extensive experiments with models such as GPT-O1, GPT-4O, GPT-3.5, DeepSeek, and other smaller models like Perplexity AI (7B)
  arXiv  Detail & Related papers  (2024-05-03T16:38:51Z)
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection [64.4610684475899]
  FactCHD is a benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. We introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2.
  arXiv  Detail & Related papers  (2023-10-18T16:27:49Z)
• Generating Benchmarks for Factuality Evaluation of Language Models [61.69950787311278]
  We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score and perplexity do not always agree on model ranking; (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation.
  arXiv  Detail & Related papers  (2023-07-13T17:14:38Z)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)
• SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables [68.76415918462418]
  We present SCITAB, a challenging evaluation dataset consisting of 1.2K expert-verified scientific claims. Through extensive evaluations, we demonstrate that SCITAB poses a significant challenge to state-of-the-art models. Our analysis uncovers several unique challenges posed by SCITAB, including table grounding, claim ambiguity, and compositional reasoning.
  arXiv  Detail & Related papers  (2023-05-22T16:13:50Z)
• Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors [80.22825549235556]
  Existing approaches can not synchronously consider error position and type. We build an FG-TED model to predict the textbf addition and textbfomission errors. Experiments show that our model can identify both error type and position concurrently, and gives state-of-the-art results.
  arXiv  Detail & Related papers  (2023-02-17T16:20:33Z)
• On the Blind Spots of Model-Based Evaluation Metrics for Text Generation [79.01422521024834]
  We explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics. We design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics.
  arXiv  Detail & Related papers  (2022-12-20T06:24:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.