3 citations · 12 across the 19 of their papers we have counts for
22 papers
CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds
Ang Li, Haofei Yu, Zhengwei Tao +7
The growing use of language models for forecasting motivates a broader evaluation of their world-model capabilities. Such evaluation must go beyond predictive accuracy to assess hi…
ROSS: Relearning from Self-Generated Rollouts through Selective Supervision
Zhiwei Zhang, Huayu Deng, Fei Zhao +4
Large language model post-training generates self-generated rollouts through reinforcement learning and on-policy distillation, yet this experience is often treated as stale once t…
Verify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation
Zhiwei Zhang, Zechen Sun, Fei Zhao +6
On-policy distillation (OPD) accelerates post-training by providing dense token-level supervision from a frozen teacher on the student's own rollouts. Vanilla OPD applies this supe…
World Model-Guided Reinforcement Learning via Counterfactual User Engagement Simulation
Ang Li, Xin Xu, Bin Liang +4
Reinforcement learning for user-centric agents is limited by the cost, latency, and risk of collecting online feedback, as well as by the lack of counterfactual comparisons under t…
Not All or None: Dynamic Construction of Target-aware Memory Graph for Conversational Stance Detection
Yifan Xiang, Bin Liang, Yuqi Huang +2
Stance detection is crucial for understanding the underlying attitude of an expression towards a target. Conversational stance detection is a more challenging stance detection task…
RefVideo-6M: A Reliable Reference-Based Dataset for Instructional Video Editing
Bojia Zi, Xiaoyan Yang, Yu Zhou +7
Recent advances in video editing have been largely driven by large-scale instruction-based datasets. However, existing datasets still suffer from two critical limitations. First, t…
Write, Execute, Refine: From Skill Follower to Skill Optimizer via Reinforcement Learning from Execution Feedback
Kang Peng, Zhiwei Zhang, Yichen Zhang +7
Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8--11 points worse than using no skill. While inference-time loops can repai…
Expectation Alignment of Language Models for Real-World User Expectations
Miaomiao Li, Yang Wang, Bin Liang +3
Large language models (LLMs) have demonstrated remarkable performance on standard benchmarks, yet it remains largely unexplored whether they truly meet user expectations. Existing…
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors
Zhiwei Zhang, Fei Zhao, Rui Wang +6
Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: after a tool-call error, smaller models often fall into repetitive invalid…
ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
Yiming Du, Yifan Xiang, Bin Liang +3
Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early…
MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents
Yiming Du, Bingbing Wang, Yang He +7
Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities fo…
Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists
Bojia Zi, Penghui Ruan, Marco Chen +7
Recent advancements in video generation have spurred the development of video editing techniques, which can be divided into inversion-based and end-to-end methods. However, current…
PEARL: Towards Permutation-Resilient LLMs
Liang Chen, Li Shen, Yang Deng +3
The in-context learning (ICL) capability of large language models (LLMs) enables them to perform challenging tasks using provided demonstrations. However, ICL is highly sensitive t…
OSPC: Detecting Harmful Memes with Large Language Model as a Catalyst
Jingtao Cao, Zheng Zhang, Hongru Wang +3
Memes, which rapidly disseminate personal opinions and positions across the internet, also pose significant challenges in propagating social bias and prejudice. This study presents…
A Comprehensive Evaluation on Event Reasoning of Large Language Models
Zhengwei Tao, Zhi Jin, Yifan Zhang +7
Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of th…
CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
Bojia Zi, Shihao Zhao, Xianbiao Qi +6
Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks…
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
Rui Wang, Fei Mi, Yi Chen +5
The growing interest in Large Language Models (LLMs) for specialized applications has revealed a significant challenge: when tailored to specific domains, LLMs tend to experience c…
Towards Real-World Stickers Use: A New Dataset for Multi-Tag Sticker Recognition
Bingbing Wang, Bin Liang, Chun-Mei Feng +6
In real-world conversations, the diversity and ambiguity of stickers often lead to varied interpretations based on the context, necessitating the requirement for comprehensively un…
Reply with Sticker: New Dataset and Model for Sticker Retrieval
Bin Liang, Bingbing Wang, Zhixin Bai +6
Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a viv…
Multi-modal Stance Detection: New Datasets and Model
Bin Liang, Ang Li, Jingqian Zhao +5
Stance detection is a challenging task that aims to identify public opinion from social media platforms with respect to specific targets. Previous work on stance detection largely…
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration
Ang Li, Jingqian Zhao, Bin Liang +6
Stance detection is critical for understanding the underlying position or attitude expressed toward a topic. Large language models (LLMs) have demonstrated significant advancements…
COPR: Continual Human Preference Learning via Optimal Policy Regularization
Han Zhang, Lin Gui, Yu Lei +8
Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences. Given the evolving nature of…