All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection
- URL: http://arxiv.org/abs/2601.04160v3
- Date: Fri, 09 Jan 2026 05:56:48 GMT
- Title: All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection
- Abstract summary: RFC Bench is a benchmark for evaluating large language models on financial misinformation under realistic news.<n>The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis.
- Score: 67.89888669159899
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce RFC Bench, a benchmark for evaluating large language models on financial misinformation under realistic news. RFC Bench operates at the paragraph level and captures the contextual complexity of financial news where meaning emerges from dispersed cues. The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis using paired original perturbed inputs. Experiments reveal a consistent pattern: performance is substantially stronger when comparative context is available, while reference free settings expose significant weaknesses, including unstable predictions and elevated invalid outputs. These results indicate that current models struggle to maintain coherent belief states without external grounding. By highlighting this gap, RFC Bench provides a structured testbed for studying reference free reasoning and advancing more reliable financial misinformation detection in real world settings.
Related papers
- Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks [7.094237287509504]
We propose a robust FSFD framework leveraging Large Language Models (LLMs) to integrate structured financial data and unstructured textual information from financial reports.<n>We construct and make publicly available a comprehensive U.S. company dataset combining financial statements, summarized MD&A text, and fraud labels.<n>Our approach achieves the best performance on the challenging CI-FSFD task, demonstrating the critical value of textual data and robust evaluation for reliable financial fraud detection.
arXiv Detail & Related papers (2026-07-21T16:32:54Z) - The Illusion of Robustness: Aggregate Accuracy Hides Prediction Flips under Task-Irrelevant Context [76.36635039699112]
Large language models (LLMs) are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context.<n>We find that state-of-the-art models often appear robust to task-irrelevant context at the aggregate level.<n>This aggregate stability, however, masks significant per-example instability.
arXiv Detail & Related papers (2026-07-14T17:01:12Z) - LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
arXiv Detail & Related papers (2026-05-02T11:31:33Z) - MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events [0.7685926099671206]
Machine learning in high-stakes domains such as healthcare requires strong predictive performance and reliable uncertainty quantification.<n>We introduce MADE, a living MLTC benchmark derived from medical device adverse event reports.<n>We systematically assess entropy-/consistency-based and self-verbalized UQ methods.
arXiv Detail & Related papers (2026-04-16T16:28:16Z) - Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models [4.8239913268889]
This paper presents our winning methodology for the "Reference-Free Financial Misinformation Detection" shared task.<n>We propose a comprehensive framework that capitalizes on the reasoning capabilities of state-of-the-art Large Language Models.<n>Our proposed system demonstrated superior efficacy, successfully securing the first-place ranking on both official leaderboards.
arXiv Detail & Related papers (2026-04-16T05:38:58Z) - LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection [11.420370070522205]
We introduce LiveFact, a benchmark that simulates the real-world "fog of war" in misinformation detection.<n>LiveFact uses dynamic, temporal evidence sets to evaluate models on their ability to reason with evolving, incomplete information.<n>Tests show that open-source Mixture-of-Experts models, such as Qwen3-235B-A22B, now match or outperform proprietary state-of-the-art systems.
arXiv Detail & Related papers (2026-04-06T16:20:47Z) - Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation [1.405010905897415]
Traditional metrics like BLEU and BERTScore fail to capture semantic fidelity in generative text-to-text tasks.<n>We adapt the Cross-Examination Framework (CEF) for a reference-free, multi-dimensional evaluation.<n>CEF generates verifiable questions from each text and performs a cross-examination to derive three interpretable scores: Coverage, Conformity, and Consistency.
arXiv Detail & Related papers (2026-01-27T08:30:13Z) - AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domains [3.721111684544962]
Hallucination in large language models (LLMs) contributes to spread of misinformation and diminished public trust.<n>We introduce AdversaRiskQA, the first verified and reliable benchmark systematically evaluating adversarial factuality.<n>We evaluate six open- and closed-source LLMs from the Qwen, GPT-OSS, and GPT families, measuring misinformation detection rates.
arXiv Detail & Related papers (2026-01-21T22:47:59Z) - PaperAudit-Bench: Benchmarking Error Detection in Research Papers for Critical Automated Peer Review [54.141490756509306]
We introduce PaperAudit-Bench, which consists of two components: PaperAudit-Dataset, an error dataset, and PaperAudit-Review, an automated review framework.<n>Experiments on PaperAudit-Bench reveal large variability in error detectability across models and detection depths.<n>We show that the dataset supports training lightweight LLM detectors via SFT and RL, enabling effective error detection at reduced computational cost.
arXiv Detail & Related papers (2026-01-07T04:26:12Z) - ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning [2.1461777157838724]
We introduce ReasonBENCH, the first benchmark designed to quantify the underlying instability in large language models (LLMs) reasoning.<n>Across tasks from different domains, we find that the vast majority of reasoning strategies and models exhibit high instability.<n>We further analyze the impact of prompts, model families, and scale on the trade-off between solve rate and stability.
arXiv Detail & Related papers (2025-12-08T18:26:58Z) - TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
arXiv Detail & Related papers (2025-04-20T09:20:55Z) - Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions [75.45274978665684]
Vision-Language Understanding (VLU) benchmarks contain samples where answers rely on assumptions unsupported by the provided context.<n>We collect contextual data for each sample whenever available and train a context selection module to facilitate evidence-based model predictions.<n>We develop a general-purpose Context-AwaRe Abstention detector to identify samples lacking sufficient context and enhance model accuracy.
arXiv Detail & Related papers (2024-05-18T02:21:32Z) - AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation [57.8363998797433]
We propose AMRFact, a framework that generates perturbed summaries using Abstract Meaning Representations (AMRs)
Our approach parses factually consistent summaries into AMR graphs and injects controlled factual inconsistencies to create negative examples, allowing for coherent factually inconsistent summaries to be generated with high error-type coverage.
arXiv Detail & Related papers (2023-11-16T02:56:29Z) - Prototype-based Aleatoric Uncertainty Quantification for Cross-modal
Retrieval [139.21955930418815]
Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space.
However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts.
We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
arXiv Detail & Related papers (2023-09-29T09:41:19Z) - Measuring Consistency in Text-based Financial Forecasting Models [10.339586273664725]
FinTrust is an evaluation tool that assesses logical consistency in financial text.
We show that the consistency of state-of-the-art NLP models for financial forecasting is poor.
Our analysis of the performance degradation caused by meaning-preserving alternations suggests that current text-based methods are not suitable for robustly predicting market information.
arXiv Detail & Related papers (2023-05-15T10:32:26Z)
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.