SiDyP improves classifiers trained on LLM-generated noisy labels by retrieving likely true labels from embedding-space neighbors and iteratively refining them with a simplex diffusion model, reporting average gains of 7.21% (zero-shot) and 7.30% (few-shot).
Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
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abstract
In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of optimism. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.
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Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
SiDyP improves classifiers trained on LLM-generated noisy labels by retrieving likely true labels from embedding-space neighbors and iteratively refining them with a simplex diffusion model, reporting average gains of 7.21% (zero-shot) and 7.30% (few-shot).