2 citations · 3 across the 11 of their papers we have counts for
12 papers
Steering Visual Generation in Unified Multimodal Models with Understanding Supervision
Zeyu Liu, Zanlin Ni, Yang Yue +5
Unified multimodal models are envisioned to bridge the gap between understanding and generation. Yet, to achieve competitive performance, state-of-the-art models adopt largely deco…
ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning
Yihong Tang, Jiao Ou, Che Liu +3
Role-playing is an emerging application in the field of Human-Computer Interaction (HCI), primarily implemented through the alignment training of a large language model (LLM) with…
Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector
Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao +5
Hallucination detection is a challenging task for large language models (LLMs), and existing studies heavily rely on powerful closed-source LLMs such as GPT-4. In this paper, we pr…
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
Jiao Ou, Jiayu Wu, Che Liu +3
Aligning large language models (LLMs) with human expectations requires high-quality instructional dialogues, which usually require instructions that are diverse and in-depth. Exist…
DragAnything: Motion Control for Anything using Entity Representation
Weijia Wu, Zhuang Li, Yuchao Gu +7
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control m…
Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models
Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang +4
Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that L…
Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement
Yihong Tang, Jiao Ou, Che Liu +3
The advent of Large Language Models (LLMs) has propelled dialogue generation into new realms, particularly in the field of role-playing systems (RPSs). While enhanced with ordinary…
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint
Zhipeng Chen, Kun Zhou, Wayne Xin Zhao +4
Reinforcement learning (RL) has been widely used in training large language models (LLMs) for preventing unexpected outputs, eg reducing harmfulness and errors. However, existing R…
Paragraph-to-Image Generation with Information-Enriched Diffusion Model
Weijia Wu, Zhuang Li, Yefei He +6
Text-to-image (T2I) models have recently experienced rapid development, achieving astonishing performance in terms of fidelity and textual alignment capabilities. However, given a…
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Lei Lin, Jiayi Fu, Pengli Liu +7
Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting…
DialogBench: Evaluating LLMs as Human-like Dialogue Systems
Jiao Ou, Junda Lu, Che Liu +4
Large language models (LLMs) have achieved remarkable breakthroughs in new dialogue capabilities by leveraging instruction tuning, which refreshes human impressions of dialogue sys…
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models
Yuchong Sun, Che Liu, Kun Zhou +6
Humans often interact with large language models (LLMs) in multi-turn interaction to obtain desired answers or more information. However, most existing studies overlook the multi-t…