arXiv Machine Learning August 11, 2026 By Gregorius Reynaldi Pratama, Kuo-Kun Tseng

Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk

Read the original on arXiv Machine Learning →

arXiv:2608. 08126v1 Announce Type: new Abstract: Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters, in tension with supervisory expectations that adverse decisions be explainable.

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

llms

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Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models

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benchmarks

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llms fine-tuning

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llms benchmarks

arXiv Machine Learning

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llms reinforcement-learning

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Split the Labor: Separating Evidence Interpretation from Decision Aggregation

Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models

When Single-Dataset Conclusions Fail: A 45-Task Study of Threshold Tuning and Resampling for Imbalanced Classification

Intelligence Is Not the Bottleneck: Validating an LLM First-Pass Manuscript Score Against Peer-Review Outcomes

LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning

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