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natural language processing

7,775 papers

#natural language process... Preprint Sep 2026

Layer-Informed Fine-Tuning via Three-Stage Functional Segmentation of LLMs

In recent years, the performance of large language models (LLMs) on reasoning tasks has been remarkable, even surpassing human capabilities on various benchmarks. However, there remains a lack of clear understanding in the academic community regarding how the structure and internal parameters of LLMs progressively solv...

Jun-Ning Shao, Si-Wei Wang, Zhi-Xuan Fang · 0 citations
#natural language process... Preprint Sep 2026

Zero-shot Dependency Parsing with Unsupervised Cross-Lingual Bootstrapping

Pre-trained language models (PLMs) with encoder-based architectures have shown impressive capabilities in zero-shot cross-lingual transfer for various language understanding tasks. However, applying this technique to dependency parsing remains a significant challenge due to its syntactic nature. To boost model generali...

Lalita Lowphansirikul, Attapol Rutherford, Jian Gang Ngui et al. · 0 citations
#natural language process... Preprint Sep 2026

AnthroDial: Benchmarking LLM Anthropomorphism in Autonomous Social Interaction

AnthroDial is introduced, a unified framework for developing anthropomorphic social agents from three complementary aspects: MindFlow, a lightweight interaction harness that enables autonomous, asynchronous, and adaptive communication through a dynamic Mind Buffer; CAPS-Eval, a theory-grounded framework for evaluating...

Wen-Tao Liu, Xi Chen, Si-Yu Song et al. · 0 citations
#natural language process... Preprint Open access Sep 2026

Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment

Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly elicit unsafe responses. Existing safety methods often require large preference datasets, costly multi-rollout training, or additional safeguards at inference time. They m...

Haotian Deng, Wenbin Xing, Gang Xu et al. · 0 citations
#natural language process... Preprint Sep 2026

Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings

Large language models (LLMs) have shown strong potential as training-free text encoders for long-context embeddings. Existing approaches primarily improve information flow under causal attention and typically construct embeddings by uniformly averaging all token representations. However, for long documents, such mean p...

Zi-Feng Cheng, Jie Zheng, Zhiwei Jiang et al. · 0 citations
#natural language process... Preprint Open access Sep 2026

Which papyrus HTR is good enough? Character-error-rate tolerance of four papyrological tasks on Greek texts

Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would let scholars discover documents and literary works that have so far gone unread. Recognition systems for Ancient Greek papyri are in statu nascendi, and how accurate the...

Anton Repushko, Elena Chepel · 0 citations
#natural language process... Preprint Open access Sep 2026

Billiger.de Products: A Bilingual Entity Matching Benchmark

Existing product matching benchmarks primarily contain English-language product data and are often dominated by a single product category, such as electronics. This paper introduces Billiger.de Products, a bilingual German and English entity matching benchmark covering thirteen consumer product categories, including di...

Aaron Steiner, Ksenia Elagin, Ralph Peeters et al. · 0 citations
#natural language process... Preprint Open access Sep 2026

Reader Proficiency Shapes Layer-wise Surprisal Profiles

Reading behaviour varies not only with linguistic input, but also with reader proficiency. In this study, we investigate whether the layer-wise relationship between surprisal from large language models (LLMs) and human gaze behaviour differs across readers with different levels of proficiency and across gaze measures....

Akio Hayakawa, Horacio Saggion · 0 citations
#natural language process... Preprint Sep 2026

Corpus-Guided Dual-Path Propagation for Graph Retrieval-Augmented Generation

Graph-based retrieval-augmented generation supports multi-hop retrieval by organizing corpus information into graphs. However, existing relation-free graph retrieval methods rely primarily on query-sentence similarity to search for evidence. This can exclude useful bridging evidence with low query similarity and activa...

Ba-Xian Liu, Tong Wei · 0 citations
#natural language process... Preprint Sep 2026

Co-Linguistics: AI-augmented Theory Construction in Linguistics

LLMs have been studied in recent linguistics as potential models of humans'linguistic abilities. Here we discuss an entirely different use of AI, namely as a co-scientist, to help construct and assess linguistic theories (we refer to the result as"Co-Linguistics"). Since the 1960s, linguistics has developed theories th...

E. Chemla, B. Spector, Alexandros Kalomoiros et al. · 0 citations
#natural language process... Preprint Open access Sep 2026

Devils in Question Relay: Source-Conditioned Relay Steering to Mitigate Hallucinations in Audio-visual Large Language Models

Audio-visual large language models (AVLLMs) have made remarkable progress in multimodal understanding and reasoning through interactions among visual, auditory, and linguistic information. However, recent studies show that AVLLMs face a critical challenge: $\textbf{source-confused grounding hallucination}$, where cues...

Yu Zhang, Pingrui Zhang, Xuefeng Bai et al. · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

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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