SenseAI: A Human-in-the-Loop Dataset for RLHF-Aligned Financial Sentiment Reasoning
arXiv:2604.05135v1 Announce Type: new Abstract: We introduce SenseAI, a human-in-the-loop (HITL) validated financial sentiment dataset designed to capture not only model outputs but the full reasoning process behind them. Unlike existing resources, SenseAI incorporates reasoning chains, confidence scores, human correction signals, and real-world market outcomes, providing a structure aligned with Reinforcement Learning from Human Feedback (RLHF) paradigms. The dataset consists of 1,439 labelled data points across 40 US-listed equities and 13 financial data categories, enabling direct integration into modern LLM fine-tuning pipelines. Through analysis, we identify several systematic patterns in model behavior, including a novel failure mode we term Latent Reasoning Drift, where models introduce information not grounded in the input, as well as consistent confidence miscalibration and forward projection tendencies. These findings suggest that LLM errors in financial reasoning are no
arXiv:2604.05135v1 Announce Type: new Abstract: We introduce SenseAI, a human-in-the-loop (HITL) validated financial sentiment dataset designed to capture not only model outputs but the full reasoning process behind them. Unlike existing resources, SenseAI incorporates reasoning chains, confidence scores, human correction signals, and real-world market outcomes, providing a structure aligned with Reinforcement Learning from Human Feedback (RLHF) paradigms. The dataset consists of 1,439 labelled data points across 40 US-listed equities and 13 financial data categories, enabling direct integration into modern LLM fine-tuning pipelines. Through analysis, we identify several systematic patterns in model behavior, including a novel failure mode we term Latent Reasoning Drift, where models introduce information not grounded in the input, as well as consistent confidence miscalibration and forward projection tendencies. These findings suggest that LLM errors in financial reasoning are not random but occur within a predictable and correctable regime, supporting the use of structured HITL data for targeted model improvement. We discuss implications for financial AI systems and highlight opportunities for applying SenseAI in model evaluation and alignment.
Executive Summary
The article presents SenseAI, a novel human-in-the-loop (HITL) financial sentiment dataset designed to align with Reinforcement Learning from Human Feedback (RLHF) paradigms. Unlike traditional datasets, SenseAI captures reasoning chains, confidence scores, human correction signals, and real-world market outcomes, encompassing 1,439 labeled data points across 40 US-listed equities and 13 financial categories. The authors identify systematic patterns in model behavior, including a newly termed failure mode—Latent Reasoning Drift—and highlight consistent confidence miscalibration and forward projection tendencies. The findings suggest that LLM errors in financial reasoning are not random but occur within predictable regimes, supporting the use of structured HITL data for targeted model improvement. The dataset is positioned as a valuable resource for financial AI systems, model evaluation, and alignment processes, offering a rigorous framework for enhancing LLM performance in high-stakes financial contexts.
Key Points
- ▸ SenseAI is a HITL-validated financial sentiment dataset that captures reasoning chains, confidence scores, human corrections, and market outcomes, aligning with RLHF paradigms.
- ▸ The dataset consists of 1,439 labeled data points across 40 equities and 13 financial categories, enabling direct integration into LLM fine-tuning pipelines.
- ▸ The authors identify systematic error patterns in LLM financial reasoning, including a novel failure mode termed Latent Reasoning Drift, confidence miscalibration, and forward projection tendencies, suggesting errors are predictable and correctable.
Merits
Innovative Dataset Structure
SenseAI uniquely integrates reasoning chains, confidence scores, and human correction signals, providing a more comprehensive and structured approach to financial sentiment analysis compared to existing datasets.
Alignment with RLHF Paradigms
The dataset is explicitly designed to align with RLHF frameworks, making it highly relevant for modern LLM fine-tuning and alignment processes in high-stakes domains like finance.
Empirical Rigor and Systematic Insights
The identification of systematic error patterns, such as Latent Reasoning Drift, offers actionable insights into model behavior, enabling targeted improvements in financial AI systems.
Demerits
Limited Data Scope
The dataset consists of only 1,439 labeled data points across 40 equities and 13 financial categories, which may limit its generalizability to broader financial markets or more diverse asset classes.
Dependence on Human Annotation
The reliance on human-in-the-loop validation introduces potential biases and scalability challenges, as human annotation is resource-intensive and subject to inter-annotator variability.
Market Outcome Integration Challenges
While integrating real-world market outcomes is a strength, the temporal lag between model predictions and market reactions may complicate the direct evaluation of model performance and reasoning chains.
Expert Commentary
The introduction of SenseAI represents a significant advancement in the intersection of financial NLP and AI alignment. By embedding human-in-the-loop validation and capturing the full reasoning process, the authors address a critical gap in existing datasets, which often prioritize model outputs over the underlying logic. The identification of Latent Reasoning Drift is particularly noteworthy, as it highlights a subtle yet pervasive failure mode in financial AI systems—one that could have significant implications for risk management and decision-making in real-world markets. The dataset’s alignment with RLHF paradigms is timely, given the growing emphasis on human alignment in LLM development. However, the practical deployment of SenseAI will require addressing scalability challenges, particularly in expanding the dataset’s breadth and depth to encompass a wider range of financial instruments and market conditions. Additionally, the integration of real-world market outcomes, while valuable, introduces complexities related to temporal dynamics and causality, which warrant further exploration. Overall, SenseAI sets a new standard for financial sentiment analysis datasets and offers a robust framework for advancing both research and practice in financial AI.
Recommendations
- ✓ Expand the dataset to include a broader range of financial instruments, asset classes, and market conditions to enhance generalizability and robustness.
- ✓ Develop standardized protocols for human annotation to mitigate biases and ensure consistency in reasoning chains and confidence scores across annotators.
- ✓ Conduct longitudinal studies to evaluate the long-term impact of SenseAI-integrated LLM fine-tuning on financial AI system performance and alignment with real-world market outcomes.
Sources
Original: arXiv - cs.CL