2 citations · 3 across the 13 of their papers we have counts for
5 papers · 1 filter
Strategy Accumulation and Guided Execution for Automated LLM Fine-Tuning
Haoran Zhao, Wei Du, Dingwen Yang +7
Producing task-specific large language models requires discovering effective training strategies through experimentation. Automated fine-tuning systems have made this experimentati…
Reinforcement Learning from Denoising Feedback
Qi He, Huan Chen, Ya Guo +3
Policy loss estimation remains a fundamental and long-standing challenge in reinforcement learning (RL) for diffusion language models (DLMs). We introduce Reinforcement Learning fr…
Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs
Zixuan Chen, Hao Lin, Zizhe Chen +6
LLMs reliably correct false claims when presented in isolation, yet when the same claims are embedded in task-oriented requests, they often comply rather than correct. We term this…
AVID: A Benchmark for Omni-Modal Audio-Visual Inconsistency Understanding via Agent-Driven Construction
Zixuan Chen, Depeng Wang, Hao Lin +6
We present AVID, the first large-scale benchmark for audio-visual inconsistency understanding in videos. While omni-modal large language models excel at temporally aligned tasks su…
Up to 36x Speedup: Mask-based Parallel Inference Paradigm for Key Information Extraction in MLLMs
Xinzhong Wang, Ya Guo, Jing Li +5
Key Information Extraction (KIE) from visually-rich documents (VrDs) is a critical task, for which recent Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs)…