2 citations · 3 across the 9 of their papers we have counts for
9 papers
Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions
Bo Zhao, Kairui Guo, Runnan Du +6
Instruction guided image editing has advanced substantially with recent generative models, yet it still fails to produce reliable results across many seemingly simple cases. We obs…
XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning
Zhihan Zhang, Yixin Cao, Lizi Liao
Solving financial problems demands complex reasoning, multimodal data processing, and a broad technical understanding, presenting unique challenges for current large language model…
Good Learners Think Their Thinking: Generative PRM Makes Large Reasoning Model More Efficient Math Learner
Tao He, Rongchuan Mu, Lizi Liao +3
Large reasoning models (LRMs) have recently shown promise in solving complex math problems when optimized with Reinforcement Learning (RL). But conventional approaches rely on outc…
Com: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models
Kai Xiong, Xiao Ding, Yixin Cao +7
Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple common…
Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement
Zhihan Zhang, Yixin Cao, Lizi Liao
Translating chart images into executable plotting scripts-referred to as the chart-to-code generation task-requires Multimodal Large Language Models (MLLMs) to perform fine-grained…
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues
Tao He, Lizi Liao, Yixin Cao +5
Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional t…
Planning Like Human: A Dual-process Framework for Dialogue Planning
Tao He, Lizi Liao, Yixin Cao +4
In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typic…
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Kai Xiong, Xiao Ding, Ting Liu +5
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking huma…
VEML: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
Tao He, Ming Liu, Yixin Cao +5
Knowledge Graph Completion (KGC) aims to reason over known facts and infer missing links but achieves weak performances on those sparse Knowledge Graphs (KGs). Recent works introdu…