#probability-calibration News & Analysis

3 articles tagged with #probability-calibration. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · Jun 117/10

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WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

Researchers introduce WorldReasoner, an evaluation framework that assesses whether language model agents can genuinely forecast real-world events through valid reasoning rather than memorization or fabrication. The framework evaluates forecasts across three dimensions—outcome accuracy, evidence quality, and causal reasoning—using 345 resolved tasks built from over 14,000 articles, revealing that agents struggle to convert grounded evidence into properly calibrated probabilities despite improvements in temporally valid retrieval.

AIBullisharXiv – CS AI · Apr 157/10

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IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration

Researchers introduce IDEA, a framework that converts Large Language Model decision-making into interpretable, editable parametric models with calibrated probabilities. The approach outperforms major LLMs like GPT-5.2 and DeepSeek R1 on benchmarks while enabling direct expert knowledge integration and precise human-AI collaboration.

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AINeutralarXiv – CS AI · May 286/10

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Probability-Entropy Calibration: An Elastic Indicator for Adaptive Fine-tuning

RankTuner, a new fine-tuning mechanism, introduces probability-entropy calibration to improve supervised learning in large language models. By combining ground-truth probability with token entropy metrics through a Relative Rank Indicator, the approach achieves better performance on mathematical reasoning and code generation tasks compared to single-metric baselines.