The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications

• URL: http://arxiv.org/abs/2604.24668v2
• Date: Tue, 28 Apr 2026 18:11:32 GMT
• Title: The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
• Abstract summary: We focus on evaluating sycophancy that LLMs display in agentic financial tasks.<n>Our findings are three-fold: first, we find the models show only low to modest drops in performance in the face of user rebuttals or contradictions to the reference answer.<n>Second, we introduce a suite of tasks to test for sycophancy by user preference information that contradicts the reference answer and find that most models fail in the presence of such inputs.
• Score: 5.019664088524701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode that LLMs frequently display in general domain settings is that of sycophancy. That is, models prioritize agreement with expressed user beliefs over correctness, leading to decreased accuracy and trust. In this work, we focus on evaluating sycophancy that LLMs display in agentic financial tasks. Our findings are three-fold: first, we find the models show only low to modest drops in performance in the face of user rebuttals or contradictions to the reference answer, which distinguishes sycophancy that models display in financial agentic settings from findings in prior work. Second, we introduce a suite of tasks to test for sycophancy by user preference information that contradicts the reference answer and find that most models fail in the presence of such inputs. Lastly, we benchmark different modes of recovery such as input filtering with a pretrained LLM.

Related papers

• 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)
• Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap [4.950224736359097]
  Atropos is a predictive early-termination analysis and hotswap technique.<n>It aims to improve the cost-benefit trade-off for LLM-based agents that use self-consistency.<n>ATROPOS achieves 74.35% of the performance of closed LLMs with as low as only 23.9% of the cost.
  arXiv  Detail & Related papers  (2026-04-16T14:39:36Z)
• Verbalizing LLMs' assumptions to explain and control sycophancy [62.927670321859495]
  LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing genuine assessment.<n>We present Verbalized Assumptions, a framework for eliciting these assumptions from LLMs.<n>Our work contributes a new understanding of assumptions as a mechanism for sycophancy.
  arXiv  Detail & Related papers  (2026-04-03T14:15:43Z)
• Interpreting LLMs as Credit Risk Classifiers: Do Their Feature Explanations Align with Classical ML? [4.0057196015831495]
  Large Language Models (LLMs) are increasingly explored as flexible alternatives to classical machine learning models for classification tasks through zero-shot prompting.<n>This study conducts a systematic comparison between zero-shot LLM-based classifiers and LightGBM, a state-of-the-art gradient-boosting model, on a real-world loan default prediction task.<n>We evaluate their predictive performance, analyze feature attributions using SHAP, and assess the reliability of LLM-generated self-explanations.
  arXiv  Detail & Related papers  (2025-10-29T17:05:00Z)
• Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents [40.636918662488505]
  LLM-based financial agents have attracted widespread excitement for their ability to trade like human experts.<n>Most systems exhibit a "profit mirage": dazzling back-tested returns evaporate once the model's knowledge window ends.<n>We introduce FactFin, a framework that applies counterfactual perturbations to compel LLM-based agents to learn causal drivers instead of memorized outcomes.
  arXiv  Detail & Related papers  (2025-10-09T08:13:35Z)
• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [62.388554963415906]
  In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data.<n>These conflicts are especially problematic in real-world investment services, where a model's inherent biases can misalign with institutional objectives.<n>We propose an experimental framework to investigate emergent behaviors in such conflict scenarios, offering a quantitative analysis of bias in investment analysis.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• One Token to Fool LLM-as-a-Judge [52.45386385722788]
  Large language models (LLMs) are increasingly trusted as automated judges, assisting evaluation and providing reward signals for training other models.<n>We uncover a critical vulnerability even in this reference-based paradigm: generative reward models are systematically susceptible to reward hacking.
  arXiv  Detail & Related papers  (2025-07-11T17:55:22Z)
• Standard Benchmarks Fail - Auditing LLM Agents in Finance Must Prioritize Risk [31.43947127076459]
  Standard benchmarks fixate on how well large language model (LLM) agents perform in finance, yet say little about whether they are safe to deploy.<n>We argue that accuracy metrics and return-based scores provide an illusion of reliability, overlooking vulnerabilities such as hallucinated facts, stale data, and adversarial prompt manipulation.
  arXiv  Detail & Related papers  (2025-02-21T12:56:15Z)
• Preference Leakage: A Contamination Problem in LLM-as-a-judge [69.96778498636071]
  Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods.<n>In this work, we expose preference leakage, a contamination problem in LLM-as-a-judge caused by the relatedness between the synthetic data generators and LLM-based evaluators.
  arXiv  Detail & Related papers  (2025-02-03T17:13:03Z)
• From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning [91.79567270986901]
  Large Language Models (LLMs) tend to prioritize adherence to user prompts over providing veracious responses.<n>Recent works propose to employ supervised fine-tuning (SFT) to mitigate the sycophancy issue.<n>We propose a novel supervised pinpoint tuning (SPT), where the region-of-interest modules are tuned for a given objective.
  arXiv  Detail & Related papers  (2024-09-03T07:01:37Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)

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.