10 citations · 19 across the 15 of their papers we have counts for
9 papers · 1 filter
ViPER: Empowering the Self-Evolution of Visual Perception Abilities in Vision-Language Model
Juntian Zhang, Song Jin, Chuanqi Cheng +8
The limited capacity for fine-grained visual perception presents a critical bottleneck for Vision-Language Models (VLMs) in real-world applications. Addressing this is challenging…
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
Xiaoqing Zhang, Huabin Zheng, Ang Lv +5
Large language models (LLMs) have been observed to suddenly exhibit advanced reasoning abilities during reinforcement learning (RL), resembling an ``aha moment'' triggered by simpl…
The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News
Yuhan Liu, Yuxuan Liu, Xiaoqing Zhang +2
In today's digital environment, the rapid propagation of fake news via social networks poses significant social challenges. Most existing detection methods either employ traditiona…
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
Song Jin, Juntian Zhang, Yuhan Liu +6
Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interaction…
Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains
Juntian Zhang, Chuanqi cheng, Yuhan Liu +3
Vision-language models (VLMs) achieve remarkable success in single-image tasks. However, real-world scenarios often involve intricate multi-image inputs, leading to a notable perfo…
Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style
Xueran Han, Yuhan Liu, Mingzhe Li +5
Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, sim…
Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement
Xiaoqing Zhang, Yuhan Liu, Flood Sung +3
Code generation is crucial in software engineering for automating the coding process efficiently. While test-time computation methods show promise, they suffer from high latency du…
Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey
Zirui Song, Bin Yan, Yuhan Liu +4
Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, the…
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives
Xiaoqing Zhang, Ang Lv, Yuhan Liu +6
Large language models (LLMs) excel at few-shot in-context learning (ICL) without requiring parameter updates. However, as ICL demonstrations increase from a few to many, performanc…