Spotting the Sidestep: A New Benchmark for Honest Financial AI

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Author: Denis Avetisyan

Researchers have created a new dataset and methodology to better identify when artificial intelligence systems are dodging difficult questions in the realm of financial queries.

Lower training loss alone is not a reliable indicator of superior test performance, as demonstrated by the contrast between a single-model baseline and Eva-4B, suggesting that the utilization of judge-resolved samples functions as a regularization technique during training.
Lower training loss alone is not a reliable indicator of superior test performance, as demonstrated by the contrast between a single-model baseline and Eva-4B, suggesting that the utilization of judge-resolved samples functions as a regularization technique during training.

EvasionBench utilizes multi-model consensus and an LLM-as-Judge framework to improve the detection of evasive answers in financial question answering.

Detecting evasive responses in financial disclosures remains a critical challenge despite limited resources for robust benchmark development. This paper introduces EvasionBench: Detecting Evasive Answers in Financial Q&A via Multi-Model Consensus and LLM-as-Judge, a novel dataset and annotation framework designed to address this gap. By leveraging disagreement between leading language models and resolving discrepancies with an LLM-as-Judge, we demonstrate significant improvements in identifying evasive language, achieving an 81.3% accuracy with a compact 4B parameter model. Could this multi-model approach unlock more transparent and reliable financial reporting through improved automated detection of evasive communication?

Unmasking Evasive Responses in Financial AI

The growing reliance on large language models for question answering is paradoxically accompanied by a tendency towards evasive responses. Rather than directly addressing inquiries, these models frequently employ strategies such as offering ambiguous statements, changing the subject, or providing overly cautious disclaimers. This behavior, while potentially stemming from safety protocols designed to avoid generating harmful or misleading content, undermines the utility of LLMs as reliable information sources. The challenge lies in that this avoidance isn’t necessarily a refusal to answer, but a circumvention of direct engagement, making it difficult to discern whether a lack of response indicates genuine knowledge limitations or a deliberate strategy to sidestep the question. Consequently, users may be left with the impression of helpfulness without receiving concrete answers, creating a critical issue as applications expand into domains demanding factual accuracy.

The potential for evasive responses in large language models presents a considerable threat within financial applications, where precision and trustworthiness are paramount. Unlike general knowledge queries, financial questions often demand definitive answers impacting investment decisions, loan approvals, or risk assessments. An LLM’s tendency to deflect, offer ambiguous statements, or generate overly cautious phrasing can introduce significant uncertainty, leading to flawed analysis and potentially substantial financial losses. This is further complicated by the models’ capacity to appear authoritative even when providing incomplete or misleading information, making it difficult for users to discern genuine insight from carefully constructed avoidance. Consequently, the reliability of LLMs in financial contexts hinges not only on their ability to answer questions, but also on their consistent delivery of direct, unambiguous, and verifiable responses.

Detecting evasive responses from large language models presents a considerable analytical challenge, as these models don’t simply provide incorrect answers-they skillfully avoid directly addressing the question. Current detection methods struggle with nuance; distinguishing a genuine lack of knowledge from deliberate evasion requires assessing not just what is said, but how it is phrased. Researchers are exploring techniques like analyzing response length, semantic distance from the prompt, and the presence of hedging language-qualifiers that introduce uncertainty. Furthermore, the complexity increases when considering that models can exhibit varied evasion strategies, ranging from vague generalizations to irrelevant digressions, necessitating robust and adaptable detection algorithms. Successfully identifying these evasive patterns is crucial, particularly in high-stakes domains where reliance on accurate and direct information is paramount.

EvasionBench: A Ground Truth for Robust Evaluation

EvasionBench is a newly created dataset comprising 30,000 question-answer pairs specifically focused on financial topics. This dataset is designed to provide a robust benchmark for evaluating the performance of systems designed to detect adversarial ‘evasion’ attacks – attempts to manipulate a system into providing incorrect or harmful responses by subtly altering the input. The scale and focused domain of EvasionBench allow for more granular and statistically significant assessment of evasion detection capabilities compared to general-purpose datasets, facilitating research into the vulnerabilities of financial language models and the development of more resilient systems.

To establish a robust and reliable ground truth for the EvasionBench dataset, a multi-model consensus approach was utilized for labeling. This involved submitting each question-answer pair to multiple Large Language Models (LLMs) and aggregating their predictions. Rather than relying on a single model’s output, the consensus method leveraged the collective intelligence of several LLMs to mitigate individual model biases and inaccuracies. Specifically, predictions were tallied, and the label receiving the majority vote was assigned as the ground truth. This strategy aimed to improve label quality and reduce the impact of any single model’s potential vulnerabilities to adversarial prompting or inherent limitations in reasoning capabilities.

The labeling process for EvasionBench incorporated an ‘LLM-as-Judge’ strategy to address instances where initial multi-model consensus failed to produce definitive labels. Specifically, ambiguous question-answer pairs were presented to Claude Opus 4.5, which was tasked with determining the correct answer based on its internal knowledge and reasoning capabilities. This approach functioned as a tie-breaker, resolving disagreements between the contributing LLMs and ensuring a consistently labeled dataset. The use of a single, high-performing LLM as an adjudicator provided a systematic method for handling complex or nuanced cases, contributing to the overall reliability of the ground truth data.

Inter-annotator agreement for the EvasionBench dataset labels was quantitatively assessed using Cohen’s Kappa, resulting in a score of 0.835. This score indicates a high level of consistency between the labeling process and the employed multi-model consensus and LLM-as-Judge strategies. According to the Landis and Koch criteria for interpreting Kappa values, a score of 0.835 is categorized as ‘Almost Perfect’ agreement, signifying a robust and reliable labeling quality for the dataset.

Our annotation framework leverages both Claude Opus 4.5 and Gemini-3-Flash to independently label data, resolving discrepancies with Claude Opus 4.5 serving as the final arbiter.
Our annotation framework leverages both Claude Opus 4.5 and Gemini-3-Flash to independently label data, resolving discrepancies with Claude Opus 4.5 serving as the final arbiter.

Optimizing Detection Through Active Learning

Active learning was implemented to optimize the annotation process by strategically selecting data points that maximize model improvement with minimal labeling effort. This approach prioritizes samples deemed most informative for the evasion detection model, contrasting with random selection or uniform sampling. By focusing annotation resources on these high-value examples, we reduced the overall number of samples requiring manual labeling while maintaining, and ultimately improving, model performance. The efficiency gains are achieved by iteratively training the model, identifying samples where the model exhibits high uncertainty or disagreement, and then querying human annotators to label only those samples. This targeted approach significantly lowers annotation costs compared to labeling a large, undifferentiated dataset.

A query-by-committee approach was implemented to actively select data points for annotation. This involved utilizing an ensemble of Large Language Models (LLMs) and identifying instances where predictions diverged significantly across the committee. The rationale is that high disagreement among LLMs signals ambiguity or difficulty in the input sample, indicating that labeling this particular sample would yield the most information to improve model generalization. Samples exhibiting substantial disagreement were prioritized for human annotation, allowing the model to learn from challenging and uncertain cases and thereby enhance performance with fewer labeled examples.

Disagreement mining and hard sample mining were implemented as complementary strategies for focused data selection. Disagreement mining identified instances where multiple Large Language Models (LLMs) produced differing outputs, highlighting areas of model uncertainty and potential ambiguity in the training data. Concurrently, hard sample mining prioritized examples that consistently posed difficulty for the models, as determined by low confidence scores or incorrect predictions. This dual approach ensured that annotation efforts were directed towards both resolving inconsistencies between models and addressing inherently challenging cases within the EvasionBench dataset, leading to improved generalization and robustness of the evasion detection models.

The integration of active learning techniques – query-by-committee, disagreement mining, and hard sample mining – with the EvasionBench dataset resulted in a measurable performance increase in evasion detection models. Specifically, models trained with this combined approach achieved an overall accuracy of 81.3%. This represents a 2.4% improvement compared to models trained solely on data labeled with the Opus model, demonstrating the effectiveness of targeted data selection for enhancing model robustness against adversarial attacks.

Towards Reliable Financial AI: A Systemic Approach

A robust approach to building trustworthy financial applications leveraging large language models (LLMs) necessitates a multi-faceted strategy. Researchers are now combining EvasionBench – a rigorous testing framework designed to uncover vulnerabilities to adversarial prompts – with multi-model consensus and active learning techniques. This toolkit allows for the identification of potentially misleading or evasive responses from LLMs, subsequently improving reliability through cross-validation with other models. Active learning then focuses further refinement on areas where vulnerabilities are detected, creating a feedback loop that enhances the system’s ability to provide accurate and consistent financial information. The combined power of these methods moves beyond simple accuracy metrics, fostering greater confidence in LLM-driven financial tools and mitigating the risks associated with unreliable outputs.

The trustworthiness of financial applications powered by large language models hinges on their ability to consistently provide accurate and honest information. Recent research demonstrates that these models are susceptible to ‘evasive’ responses – subtly altered outputs designed to avoid directly addressing a query, potentially masking errors or biases. Successfully identifying and mitigating these evasive tactics is therefore crucial for enhancing reliability and reducing risk to users. By developing techniques to detect when a model is deflecting or offering incomplete answers, systems can be designed to prompt for clarification, cross-reference information, or flag potentially unreliable outputs. This proactive approach not only improves the quality of financial advice and analysis delivered by AI, but also fosters greater confidence in these increasingly prevalent technologies.

A newly developed 4-billion parameter model, designated Eva-4B, demonstrates a significant leap in performance regarding financial language understanding. Rigorous testing revealed a 25.1% accuracy improvement over its foundational base model, indicating a substantial refinement in its ability to process and interpret complex financial data. This advancement isn’t merely incremental; it suggests a pathway toward more dependable artificial intelligence applications within the financial sector, capable of discerning subtle nuances and avoiding misleading responses. The enhanced accuracy of Eva-4B provides a robust foundation for building systems that offer reliable insights and informed decision-making support.

The development of increasingly sophisticated artificial intelligence demands a concurrent focus on ethical considerations and responsible implementation. This research moves beyond simply enhancing computational power, instead prioritizing the creation of AI systems demonstrably aligned with human values. By establishing methods to identify and counteract manipulative inputs – those designed to elicit misleading or harmful responses – a crucial step is taken towards ensuring accountability. This isn’t merely about building ‘smarter’ AI; it’s about fostering trust and reliability, vital components for the widespread adoption of these technologies, particularly within sensitive sectors like finance where accuracy and ethical conduct are paramount. Ultimately, this work envisions a future where AI operates not as a black box, but as a transparent and dependable partner in decision-making.

The development of EvasionBench highlights a crucial principle in system design: structure dictates behavior. The framework’s reliance on multi-model consensus and the ‘LLM-as-Judge’ approach isn’t merely about achieving higher accuracy in detecting evasive financial answers; it’s about building a robust system where disagreement itself becomes a signal. As Vinton Cerf aptly stated, “What scales are clear ideas, not server power.” EvasionBench demonstrates this by focusing on identifying ambiguous responses – clear indicators of conceptual weakness – and using those insights to refine the annotation process. The scalability of this method rests on the clarity of the underlying principle: a strong system isn’t defined by computational force, but by the coherence of its components and the transparency of its decision-making process.

Looking Ahead

The introduction of EvasionBench, while a necessary step, underscores a persistent truth: detecting intent is rarely a matter of surface-level pattern recognition. The reliance on disagreement amongst strong language models, a clever heuristic, hints at a deeper challenge – the very definition of ‘evasion’ is fluid, context-dependent, and subtly shifts with the underlying financial instruments and regulatory landscapes. Simply identifying that an answer avoids direct engagement does not illuminate why, nor does it anticipate the next, more sophisticated, evasion tactic.

Future work would benefit from moving beyond the identification of evasive answers to modeling the structure of evasion itself. What are the core architectural principles that allow an answer to appear responsive while conveying minimal information? The current approach effectively treats evasion as a localized symptom. A more robust solution demands understanding it as a systemic property, an emergent behavior of the interaction between question, knowledge source, and language model.

Furthermore, the emphasis on financial Q&A, while pragmatic, highlights a broader need. Evasion is not unique to finance; it is a universal phenomenon in any information-seeking context where consequences attach to truthful answers. The principles demonstrated by EvasionBench – leveraging model disagreement for targeted annotation – likely extend to other domains, but only if the underlying assumption – that evasion manifests as a detectable structural anomaly – holds true across diverse knowledge systems.

Original article: https://arxiv.org/pdf/2601.09142.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-15 10:28