Tech
LLM Hallucination Detection via Low-rank Multilinear Decomposition
arXiv:2610.36191v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate fluent yet factually incorrect responses, raising concerns about their reliability. Recent hallucination detectors increasingly…
- Published
- (1 h ago)
- Outlet
- arXiv cs.CL
- Section
- Tech
- Length here
- Headline and 25-word snippet. The full article is at the source.
More from arXiv cs.CL and around the same time
3, 7, 14, 17, 42: Instruction Tuning Installs and Sharpens Number-Choice Heuristics in Language ModelsarXiv:2610.36178v1 Announce Type: new Abstract: We measure, without sampling error, the probability a language model assigns to each integer from 1 to N when asked for a random number. For a fixed prompt the model defines one…
AK_QMUL at ArGuard Shared Tasks: A Single Fine-Tuned JAIS for Arabic Harmful-Prompt DetectionarXiv:2610.36360v1 Announce Type: new Abstract: We present AK_QMUL's submissions to the ArGuard-2026 shared tasks on Arabic harmful-prompt detection: a binary safe/unsafe task (B1) and a seven-way harm-domain task (B2). Both…
Benchmarking Task Takeover Across Agent HandoffsarXiv:2610.36523v1 Announce Type: new Abstract: As agents collaborate on longer tasks, responsibility may pass between models before the work is complete. The receiving model must build on prior decisions, observations, and…
Beyond IceBERT: Open-Data ModernBERT Encoders for Icelandic from 12M to 1B ParametersarXiv:2610.36230v1 Announce Type: new Abstract: IceBERT, the reference encoder for Icelandic, found that a cleaned web crawl is enough to train a strong model and that starting from a multilingual model can help on some tasks. We…
CONCUR: Concordant Auxiliary Rewards for Multi-Task, Multi-Reward Reinforcement LearningarXiv:2610.36437v1 Announce Type: new Abstract: When a Large Language Model (LLM) is trained with Reinforcement Learning (RL) on multiple tasks, a task's primary reward can gate auxiliary rewards such as a length reward: on a…
Detect Before You Disambiguate: Perturbation-Based Input Ambiguity Detection in LLMsarXiv:2610.36353v1 Announce Type: new Abstract: Large language models (LLMs) often answer ambiguous inputs as if they had a single intended meaning. This is problematic because input ambiguity is an upstream source of…This page holds the headline and a short snippet as published by arXiv cs.CL, whose feed they came from. The story itself, the photography and the rights stay with them, and the button above goes straight to their site. We keep this page so the headline has a stable address and a record of when it ran.
