1.5k citations · 1.6k across the 10 of their papers we have counts for
10 papers
Improving Vision-language Models with Perception-centric Process Reward Models
Yingqian Min, Kun Zhou, Yifan Li +6
Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-language models (VLMs). However, it…
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning
Yifan Li, Yukai Gu, Yingqian Min +6
Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation…
Analyzing and Mitigating Object Hallucination: A Training Bias Perspective
Yifan Li, Kun Zhou, Wayne Xin Zhao +2
As scaling up training data has significantly improved the general multimodal capabilities of Large Vision-Language Models (LVLMs), they still suffer from the hallucination issue,…
Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
Yifan Du, Zikang Liu, Yifan Li +7
Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also…
Towards Event-oriented Long Video Understanding
Yifan Du, Kun Zhou, Yuqi Huo +7
With the rapid development of video Multimodal Large Language Models (MLLMs), numerous benchmarks have been proposed to assess their video understanding capability. However, due to…
Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models
Yifan Li, Hangyu Guo, Kun Zhou +2
In this paper, we study the harmlessness alignment problem of multimodal large language models (MLLMs). We conduct a systematic empirical analysis of the harmlessness performance o…
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation
Kun Zhou, Yifan Li, Wayne Xin Zhao +1
Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation. However, the discrete nature of text increases the difficult…
Evaluating Object Hallucination in Large Vision-Language Models
Yifan Li, Yifan Du, Kun Zhou +3
Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently explored by integrating powerful LLMs for improvi…
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li +19
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for compre…
Diffusion Models for Non-autoregressive Text Generation: A Survey
Yifan Li, Kun Zhou, Wayne Xin Zhao +1
Non-autoregressive (NAR) text generation has attracted much attention in the field of natural language processing, which greatly reduces the inference latency but has to sacrifice…