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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 2025Show all

6 papers · 1 filter

cs.CL2025

IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data

Tao Feng, Lizhen Qu, Niket Tandon +1

Causal discovery is fundamental to scientific research, yet traditional statistical algorithms face significant challenges, including expensive data collection, redundant computati…

cs.CL2025

Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search

Shuo Huang, Xingliang Yuan, Gholamreza Haffari +1

The increasing adoption of large language models (LLMs) in cloud-based services has raised significant privacy concerns, as user inputs may inadvertently expose sensitive informati…

cs.CV2025

Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation

Tao Feng, Xianbing Zhao, Zhenhua Chen +4

Recent advances in diffusion-based and autoregressive video generation models have achieved remarkable visual realism. However, these models typically lack accurate physical alignm…

cs.CL2025

Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models

Hao Yang, Lizhen Qu, Ehsan Shareghi +1

Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs) by enabling audio-based human interactions. However, recent research has revealed…

cs.CL2025

RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars

Yuncheng Hua, Lizhen Qu, Zhuang Li +3

Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. Current alignment approaches require high-quality annotations and significant…

eess.AS2025

Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning

Manh Luong, Khai Nguyen, Dinh Phung +2

Audio captioning systems face a fundamental challenge: teacher-forcing training creates exposure bias that leads to caption degeneration during inference. While contrastive methods…