Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Computation and Language

Sep 15

An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language Models

arXiv:2404.03028v4 Announce Type: replace Abstract: Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in...

By Emmy Liu, Graham Neubig, Jacob Andreas

llms nlp

arXiv Computation and Language

Sep 15

CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback

arXiv:2504.02323v5 Announce Type: replace Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored variou...

By Clayton Cohn, Ashwin T S, Naveeduddin Mohammed, Gautam Biswas

llms

arXiv Computation and Language

Sep 15

All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue

arXiv:2507.01594v2 Announce Type: replace Abstract: Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD sy...

By Shutong Feng, Hsien-chin Lin, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, Milica Ga\v{s}i\'c

llms reinforcement-learning

arXiv Computation and Language

Sep 15

Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering

arXiv:2601.19847v3 Announce Type: replace Abstract: Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often require...

By Fangan Dong, Zuming Yan, Xuri Ge, Zhiwei Xu, Mengqi Zhang, Xuanang Chen, Ben He, Xin Xin, Zhumin Chen, Ying Zhou

llms benchmarks

arXiv Computation and Language

Sep 15

DecompressionLM: Deterministic, Diagnostic, and Zero-Shot Concept Graph Extraction from Language Models

arXiv:2602.00377v3 Announce Type: replace Abstract: Existing knowledge probing methods rely on pre-defined queries, limiting extraction to known concepts. We introduce DecompressionLM, a stateless fr...

By Zhaochen Hong, Jiaxuan You

llms efficiency benchmarks safety

arXiv Computation and Language

Sep 15

Is my model perplexed for the right reason? Contrasting LLMs' Benchmark Behavior with Token-Level Perplexity

arXiv:2603.29396v2 Announce Type: replace Abstract: Standard evaluations of Large language models (LLMs) focus on task performance, offering limited insight into whether correct behavior reflects app...

By Zo\"e Prins, Samuele Punzo, Frank Wildenburg, Giovanni Cin\`a, Sandro Pezzelle

llms benchmarks safety

arXiv Computation and Language

Sep 15

HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation

arXiv:2604.09629v3 Announce Type: replace Abstract: Humor generation poses a significant challenge for Large Language Models (LLMs), because their standard training objective (next-token prediction)...

By Edward Ajayi, Prasenjit Mitra

llms reinforcement-learning fine-tuning efficiency safety

arXiv AI

Sep 15

MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use

arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol...

By Zixiang Liu, Wenrui Liu, Elsie Dai, Wenhan Yu, Lei Yu, Tong Yang, Jinjun Han, Hong Gao

llms agents benchmarks

arXiv Computation and Language

Sep 15

Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

arXiv:2606.27314v2 Announce Type: replace Abstract: To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive m...

By Hamid Reza Firoozfar, Mohammadsadegh Abolhasani, Reza Mousavi, Paul Jen-Hwa Hu

llms benchmarks safety

arXiv Computation and Language

Sep 15

Where Animacy Lives in Large Language Models: Tracing the Circuits of the Animacy Concept

arXiv:2607.20995v2 Announce Type: replace Abstract: Distinguishing animate from inanimate concepts in written language requires more than shallow text processing, as it involves recognizing complex s...

By Samuele Punzo, Giovanni Cin\`a, Sandro Pezzelle

llms

arXiv Computation and Language

Sep 15

Studying quantization trade-offs for efficient inference deployment in machine translation

arXiv:2607.29397v3 Announce Type: replace Abstract: Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low l...

By Jim Zhao, Sohir Maskey, Koen Oostermeijer, Douglas Orr, Teryn Jones

llms rag agents nlp efficiency benchmarks

arXiv Computation and Language

Sep 15

Readable, Faithful, Used: Three Dissociable Properties of Demographic Identity in a Language Model

arXiv:2608.18768v2 Announce Type: replace Abstract: Large language models are widely used to simulate survey respondents, yet their outputs are homogeneous and unfaithful to real inter-group differen...

By Fathin Difa Robbani

llms

arXiv Computation and Language

Sep 15

Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering

arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely a...

By Yifan Wang, Xinkui Lin, Yongxiu Xu, Shen Gao, Ruochen Yang, Kun Huang, Yubin Wang, Jie Wu, Wei Liu, Jian Luan, Hongbo Xu, Shuo Shang

llms rag nlp benchmarks

arXiv Computation and Language

Sep 15

Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking

arXiv:2407.02395v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but ins...

By Jiexin Wang, Liuwen Cao, Xitong Luo, Yang Cao, Zhenghao Li, Yunyi Xiao, Mengchen Zhao, Adam Jatowt, Yi Cai

llms agents benchmarks

arXiv Computation and Language

Sep 15

Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation

arXiv:2602.08696v3 Announce Type: replace-cross Abstract: Dysarthric speech recognition is limited by high speaker variability and scarce labeled data. Existing synthesis methods often couple speaker...

By Haoshen Wang, Xueli Zhong, Bingbing Lin, Jia Huang, Xingduo Pan, Shengxiang Liang, Nizhuan Wang, Wai Ting Siok

llms fine-tuning multimodal

arXiv Computation and Language

Sep 15

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents

arXiv:2606.17467v3 Announce Type: replace-cross Abstract: Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and int...

By Aaditya Pai

llms agents benchmarks

arXiv Computation and Language

Sep 15

SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs

arXiv:2608.27783v3 Announce Type: replace-cross Abstract: Speech language models (speech LLMs) can generate plausible outputs from audio that contains no usable speech evidence. We study this failure...

By Mengzhe Geng

llms multimodal benchmarks

arXiv Computation and Language

Sep 15

Bridging Network Psychometrics and Artificial Intelligence: An Ising-Potts Model with LLM-Derived Weights

arXiv:2609.08797v2 Announce Type: replace-cross Abstract: The Potts model extends the Ising model to multinomial data. We introduce a Rater Ising-Potts model that uses agreement indicators between pa...

By Matthias von Davier

llms rag efficiency

arXiv Computer Vision

Sep 15

Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understanding

arXiv:2609.13267v1 Announce Type: new Abstract: Scientific charts encode quantities in axes, legends, and geometric marks, yet large vision-language models still treat them as natural photographs. Vi...

By Alberlucia Rafael Soarez, Camila Ferreira, Daniel Kim, Mariana Costa, Alejandro Torres

llms computer-vision multimodal safety

arXiv Computer Vision

Sep 15

Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery

arXiv:2609.13332v1 Announce Type: new Abstract: Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space explor...

By Leo Thomas Ramos, Sidike Paheding, Abel A. Reyes-Angulo, Rajaneesh A., Sajinkumar K. S., Angel D. Sappa, Thomas Oommen

llms computer-vision multimodal benchmarks

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