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Researchers introduced PARCEL, a new benchmark designed to detect legal hallucinations in large language models by checking if claims are supported by cited sources.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.10971
#MachineLearning
Researchers evaluate Large Language Models for Machine Translation quality evaluation schemes by comparing their agreement with human annotators. Results show that both humans and models are challenged.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.10918
#MachineLearning
TokenBank provides financial infrastructure for AI services through structured contracts. It supports service-consumption rights, cash forwards, financing, and protection claims.
Source: arXiv
https://arxiv.org/abs/2610.11333
#MachineLearning
Plan-and-Patch introduces a diffusion language model framework for agentic planning and repair. Diffusion achieved a higher plan repair success rate than autoregressive models.
Source: arXiv
https://arxiv.org/abs/2610.10786
#MachineLearning
New research tests a sociolinguistic approach to measuring user-persona fidelity in simulations, using authorship-verification stylometry and lexicon-based analysis.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.10988
#MachineLearning
We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively.
Source: arXiv
https://arxiv.org/abs/2610.10833
#MachineLearning
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