Hugging Face Trending Papers
Diagnosing and Improving Probabilistic Reasoning in Large Language Models
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arXiv AI
4d ago
Diagnosing and Improving Probabilistic Reasoning in Large Language Models
The paper introduces a decision‑theoretic framework that splits a large language model’s decision loss into belief formation and action selection components. Using a synthetic benchmark, it evaluates how reinforcement‑learning interventions on beliefs, decisions, or both affect these components across three domains. The study finds that targeting a single component improves that part but may not transfer to others, while jointly targeting both improves both only when training and evaluation formats match.
arXiv AI
Jun 10
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
arXiv:2606. 09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable.
arXiv AI
Sep 10
Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation
The paper introduces Fact-Ablated Evaluation (FAE), a framework that iteratively removes cited evidence to test whether large language models (LLMs) adjust their fact‑checking predictions accordingly. Experiments reveal that many off‑the‑shelf LLMs rely more on internal knowledge than on the provided evidence. To address this, the authors propose REAL, a training method that uses counterfactual evidence supervision to encourage LLMs to base veracity judgments on evidence, achieving better evidence‑dependent performance across four datasets.
arXiv AI
Jun 8
How reliable are LLMs when it comes to playing dice?
arXiv:2606. 07515v1 Announce Type: cross Abstract: We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems.
arXiv AI
Jun 10
Learning Evidence Highlighting for Frozen LLMs
arXiv:2604. 22565v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts.
arXiv Machine Learning
2d ago
Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains
arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm,...