activity
20202026
most citedAdapting Large Language Models for Document-Level Machine Translation

9 citations · 31 across the 34 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CL2026

UniGuard: Multi-View Guardrail Learning for Cross-Interface Safety Generalization

Viet-Thanh Pham, Thuy-Trang Vu, Junae Kim +4

Guardrail models are increasingly used as safety layers for LLMs and agentic systems, but most are trained on fixed prompt or response interfaces and become unreliable when the sam…

cs.CR2026

Conformal Privacy Auditing: Calibrated Re-identification Attacks with Statistical Guarantees

Shuo Huang, Gholamreza Haffari, Xingliang Yuan +2

Empirical identity leakage from released text is increasingly driven by attackers that combine large language models (LLMs) with auxiliary knowledge to link documents to individual…

cs.CL2026

MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models

Manh Luong, Tamas Abraham, Junae Kim +6

Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench,…

cs.AI2026

Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents

Sukai Huang, Chenyuan Zhang, Fucai Ke +4

Instruction granularity is an important yet poorly controlled variable in language-guided embodied AI. Existing benchmarks typically pair each task with a single static instruction…

cs.SD2026

Resurfacing Paralinguistic Awareness in Large Audio Language Models

Hao Yang, Minghan Wang, Tongtong Wu +3

Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implic…

cs.AI2026

LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations

Viet-Thanh Pham, Lizhen Qu, Thuy-Trang Vu +2

Large language models (LLMs) are increasingly deployed as autonomous agents, yet evaluations focus primarily on task success rather than cultural appropriateness or evaluator relia…