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natural language processing

7,546 papers

#natural language process... Preprint Sep 2026

Evaluating Language Model Safety Across Long Adversarial Conversations

Proof-of-concept evidence that strong single-turn safety does not necessarily persist during sustained adversarial interaction is provided, highlighting the need for long-horizon evaluations and conversation-level safeguards that account for risk accumulating across turns.

Parisa Salmani, Peter R. Lewis · 0 citations
#natural language process... Preprint Sep 2026

Halluscoring 2026: The first shared task on llms hallucination detection and answer verification

The shared task is a shared task for evaluating hallucination detection and factual verification in Arabic question answering under challenging generalization settings, based on two Arabic datasets: HalluScore and HalluTruthQA.

Aisha Alansari, Abdessalam Bouchekif, A. Hasanaath et al. · 0 citations
#natural language process... Preprint Sep 2026

Framing the Narrative: Ideological Mimicry in Large Language Models

Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's stance as a relatively stable property. Real users, however, communicate political signals through their terminology, assumptions, and personal context. We investigate w...

Olivia Macmillan-Scott, Michael Jacobs, Nils W. Metternich et al. · 0 citations
#natural language process... Preprint Sep 2026

TomasuLLM: Out-of-Order Speculative Execution for LLM Agents

Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. This observation stall presents the same tension that drove out-of-order processors -- asequential interface hides work that can be predicted and started early, but a spec...

Jiang-Nan Yu, Ce-Yu Xu, Meng-Ming Li et al. · 0 citations
#natural language process... Preprint Aug 2026

Large Language Models are Approximate Survival Estimators

Survprompt is introduced, a framework that converts structured patient covariates into free-text clinical vignettes and prompts pre-trained LLMs to predict survival zero-shot, and feature ablations revealed that LLMs prioritized clinical variables similarly to specialized survival models.

Juan M. Zambrano-Chaves, Peniel N. Argaw, Risa Ueno et al. · 0 citations
#natural language process... Preprint Sep 2026

EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making

Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for plann...

Yu-Han Guo, Jin-Ming Liu, Liang Xu et al. · 0 citations
#natural language process... Preprint Sep 2026

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

Reusable Latent Correction (RLC) is proposed, which converts one-off natural-language guidance from a black-box LLM into persistent corrective experiences in the hidden space of an SLM, enabling the SLM to reuse LLM-derived corrections during inference without any online LLM calls.

Bo-Han Zhang, Li-Nan Yue, Weibo Gao et al. · 0 citations
#natural language process... Preprint Sep 2026

MGhana-ST: A Low-Resource Speech Translation Dataset for Ghanaian Languages and an Analysis of Multilingual Training Trade-offs

Comparing empirical cross-lingual transfer with typology-based similarity, it is found that transfer BLEU identifies closely interacting language pairs better than URIEL similarity, though neither predicts which varieties benefit from joint training.

Frank Lawrence Nii Adoquaye Acquaye, Eric George Parakal, Jesse Johnson et al. · 0 citations
#natural language process... Preprint Sep 2026

Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head

NAMOH, an architecture-native sparse attention mechanism that activates only its assigned tokens and performs causal attention within this subsequence, is introduced, and it is hoped this work offers a new path for scaling attention, with parameter scaling directly enabling context scaling.

Zi-Zhuo Fu, Run-Sheng Wang, Meng Li · 0 citations
#natural language process... Preprint Open access Oct 2026

Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears

The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as language support, ethics, and long-term impact on writers' voice and creativity remain underexplored. In this work, we carried out a questionnaire (N = 301...

Anastasiia Ivanova, Natalia Fedorova, Ekaterina Artemova · 0 citations
#natural language process... Preprint Sep 2026

JuryFlow: Disagreement-Guided Human-in-the-Loop Multi-Agent Evaluation

Large language models (LLMs) are increasingly deployed as automated judges for AI-generated content, yet a single judge is unreliable and even a panel of judges leaves a hard residue: when judges disagree, majority voting discards the conflict instead of resolving it. We present JuryFlow, a disagreement-guided, human-i...

Mu-Feng Yang, Jun-Wei Yu, Yepeng Ding · 0 citations
#natural language process... Preprint Open access Oct 2026

DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements

Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce DuplexAct-Bench, a bilingual benchmark that systematically covers six complementary behaviors, from interruption and yielding to proactive initiation, active silence, and...

Keyue Xing, Wentao Ding, Mengmeng Wang et al. · 0 citations

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MIT News · Artificial Intelligence Sep 24, 2026

Estimating suicide risk from text

A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.

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