#attribution-analysis News & Analysis

4 articles tagged with #attribution-analysis. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 197/10

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LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data

Researchers demonstrate that Large Language Models lack genuine self-awareness regarding their knowledge limitations when applied to clinical tabular data, using cross-model attribution divergence to detect epistemic blind spots. LLM confidence scores remain constant regardless of actual accuracy, while a novel cross-model calibrator achieves reliable uncertainty quantification without model access or retraining.

AIBearisharXiv – CS AI · May 287/10

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From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets

Researchers introduce KTD-Fin, a benchmark that addresses critical evaluation flaws in LLM trading agent testing by masking market identifiers to prevent memorization and using attribution analysis to isolate genuine alpha. Testing on 10 frontier LLM agents reveals that their trading returns stem primarily from passive market and style exposure rather than transferable investment skill.

AINeutralarXiv – CS AI · Apr 206/10

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LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance

Researchers conducted a comparative study of how large language models trained with different fine-tuning methods (full fine-tuning, LoRA, and quantized LoRA) interpret code compliance tasks. The study reveals that full fine-tuning produces more focused attribution patterns than parameter-efficient methods, and larger models develop distinct interpretive strategies despite performance gains plateauing above 7B parameters.

AINeutralarXiv – CS AI · Apr 106/10

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Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models

Researchers propose an attribution-driven approach to make encoder-based Large Language Models more transparent and trustworthy for network intrusion detection in Software-Defined Networks. By analyzing which traffic features drive model decisions, the study demonstrates that LLMs learn legitimate attack behavior patterns, addressing a critical barrier to deploying AI security tools in sensitive environments.