11 papers
LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
Haonan Wang, Jiaxiang Liu, Yurong Liu +11
Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved. In cont…
ST-Gen4D: Embedding 4D Spatiotemporal Cognition into World Model for 4D Generation
Haonan Wang, Hanyu Zhou, Tao Gu +1
Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spatiotemporal scale. Typically,…
PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
Haonan Wang, Brian Chen, Siquan Li +4
Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for rapidly adapting large language models (LLMs) to downstream tasks. Prefix-Tuning, an early and effective PEFT…
SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From
Yao Tong, Haonan Wang, Siquan Li +2
Fingerprinting Large Language Models (LLMs)is essential for provenance verification and model attribution. Existing fingerprinting methods are primarily evaluated after fine-tuning…
Lost in Adaptation: Layer-Selective Recovery of Temporal Reasoning in Video-Language Models
Zihang Fu, Haonan Wang, Jian Kang +2
Multimodal adaptation can erode temporal reasoning (TR) in video-language models (VLMs), leaving models able to perceive salient events yet unable to infer their temporal and causa…
Cog2Gen3D: Sculpturing 3D Semantic-Geometric Cognition for 3D Generation
Haonan Wang, Hanyu Zhou, Haoyue Liu +2
Generative models have achieved success in producing semantically plausible 2D images, but it remains challenging in 3D generation due to the absence of spatial geometry constraint…