arXiv AI August 11, 2026 By Adia Lumadjeng, Ilker Birbil, Erman Acar

How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making

arXiv:2608. 09433v1 Announce Type: cross Abstract: In regulated domains such as finance, a model that cannot be explained cannot be deployed, yet many interpretable classifiers defeat their own purpose by producing formulas with dozens of features that no regulator could read.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

efficiency

arXiv Machine Learning

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Shapley in Context: Explaining Financial Language with Domain Expertise

arXiv:2607. 00856v1 Announce Type: cross Abstract: In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications.

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llms

arXiv Machine Learning

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Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal

arXiv:2606. 12360v1 Announce Type: new Abstract: Language-model post-training is the main stage at which model behavior is shaped, yet it still largely involves optimization of scalar rewards that summarize diverse desiderata.

By Leon Bergen, Usha Bhalla, Sidharth Baskaran, Max Loeffler, Raphael Sarfati, Dhruvil Gala, Ryan Panwar, Santiago Aranguri, Thomas Fel, Atticus Geiger, Matthew Kowal, Siddharth Boppana, Daniel Balsam, Owen Lewis, Jack Merullo, Thomas McGrath, Ekdeep Singh Lubana

safety

Hugging Face Trending Papers

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Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal

Language-model post-training is the main stage at which model behavior is shaped, yet it still largely involves optimization of scalar rewards that summarize diverse desiderata. This abstraction gives practitioners little visibility into what their data actually teaches models, allowing spurious correlations to be learned by a model and inducing undesirable behaviors such as over-stylization and sycophancy.

safety

arXiv Machine Learning

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RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification

arXiv:2608. 09834v1 Announce Type: cross Abstract: Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making.

By Fan Zhang, Jiaming Li

llms nlp fine-tuning

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Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text

arXiv:2607. 26368v1 Announce Type: cross Abstract: Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.

By Aman Kumar, Lasitha Vidyaratne, Dipanjan D Ghosh, Arnab Chakrabarti, Ahmed K Farahat

llms rag fine-tuning benchmarks

arXiv Machine Learning

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Foundation Models for Credit Risk Prediction: A Game Changer?

arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.

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llms computer-vision nlp benchmarks

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Shapley in Context: Explaining Financial Language with Domain Expertise

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal

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