Sang-Lu Lu

Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding

A new task, Sign Language Question Answering (SLQA), which evaluates SL understanding by requiring models to answer arbitrary natural language questions about SL videos, and a simple yet effective baseline model equipped with a Question-Conditioned Modulated Temporal Downsampling module and an in-domain knowledge trans...

SignLlama: Enhancing Gloss-free Sign Language Translation by Prioritizing Visual Features for LLMs

Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks. However, fine-tuning LLMs for Gloss-Free Sign Language Translation (GFSLT) remains a challenge. In this paper, we investigate how to effectively adapt LLMs to the GFSLT task. We show that there are two key issues that need to be...

Learning Effective Sign Features without Text for Gloss-free Sign Language Translation

MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents

This work proposes MemPoison, a comprehensive benchmark and analysis framework featuring 1227 hand-validated cases across four attack types, three injection channels, and three representative memory substrates, evaluated on seven open-weight and three closed-weight model families.