37 citations · 89 across the 17 of their papers we have counts for
18 papers
T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design
Jiachen Li, Qian Long, Jian Zheng +4
In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T…
WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences
Yujie Lu, Dongfu Jiang, Wenhu Chen +3
Recent breakthroughs in vision-language models (VLMs) emphasize the necessity of benchmarking human preferences in real-world multimodal interactions. To address this gap, we launc…
TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
Weixi Feng, Jiachen Li, Michael Saxon +3
Video generation has many unique challenges beyond those of image generation. The temporal dimension introduces extensive possible variations across frames, over which consistency…
T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback
Jiachen Li, Weixi Feng, Tsu-Jui Fu +4
Diffusion-based text-to-video (T2V) models have achieved significant success but continue to be hampered by the slow sampling speed of their iterative sampling processes. To addres…
Reward Guided Latent Consistency Distillation
Jiachen Li, Weixi Feng, Wenhu Chen +1
Latent Consistency Distillation (LCD) has emerged as a promising paradigm for efficient text-to-image synthesis. By distilling a latent consistency model (LCM) from a pre-trained t…
Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
Xinyi Wang, Alfonso Amayuelas, Kexun Zhang +3
Pre-trained language models (LMs) are able to perform complex reasoning without explicit fine-tuning. To understand how pre-training with a next-token prediction objective contribu…
Interactive Natural Language Processing
Zekun Wang, Ge Zhang, Kexin Yang +19
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with t…
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
Alfonso Amayuelas, Kyle Wong, Liangming Pan +2
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their knowledge and uncertainty over questions. Specifically, we focus on a…
On the Risk of Misinformation Pollution with Large Language Models
Yikang Pan, Liangming Pan, Wenhu Chen +3
In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact…
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
Kai Nakamura, Sharon Levy, Yi-Lin Tuan +2
A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multitu…
Attacking Open-domain Question Answering by Injecting Misinformation
Liangming Pan, Wenhu Chen, Min-Yen Kan +1
With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing a…
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
Shiyang Li, Semih Yavuz, Wenhu Chen +1
Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amo…
FinQA: A Dataset of Numerical Reasoning over Financial Data
Zhiyu Chen, Wenhu Chen, Charese Smiley +8
The sheer volume of financial statements makes it difficult for humans to access and analyze a business's financials. Robust numerical reasoning likewise faces unique challenges in…
A Dataset for Answering Time-Sensitive Questions
Wenhu Chen, Xinyi Wang, William Yang Wang
Time is an important dimension in our physical world. Lots of facts can evolve with respect to time. For example, the U.S. President might change every four years. Therefore, it is…
A Systematic Investigation of KB-Text Embedding Alignment at Scale
Vardaan Pahuja, Yu Gu, Wenhu Chen +4
Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and…
Local Explanation of Dialogue Response Generation
Yi-Lin Tuan, Connor Pryor, Wenhu Chen +2
In comparison to the interpretation of classification models, the explanation of sequence generation models is also an important problem, however it has seen little attention. In t…
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
Xinyi Wang, Wenhu Chen, Michael Saxon +1
Although deep learning models have driven state-of-the-art performance on a wide array of tasks, they are prone to spurious correlations that should not be learned as predictive cl…
Zero-shot Fact Verification by Claim Generation
Liangming Pan, Wenhu Chen, Wenhan Xiong +2
Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requi…