9 papers
Reinforcement Learning Enhanced Multi-hop Reasoning for Temporal Knowledge Question Answering
Wuzhenghong Wen, Chao Xue, Su Pan +2
Temporal knowledge graph question answering (TKGQA) involves multi-hop reasoning over temporally constrained entity relationships in the knowledge graph to answer a given question.…
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models
Canhui Wu, Qiong Cao, Chang Li +5
Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks but often suffer from excessive verbosity, known as "overthinking." Existing solutions via reinforceme…
Efficient Reasoning via Thought-Training and Thought-Free Inference
Canhui Wu, Qiong Cao, Chao Xue +2
Recent advances in large language models (LLMs) have leveraged explicit Chain-of-Thought (CoT) prompting to improve reasoning accuracy. However, most existing methods primarily foc…
Layer as Puzzle Pieces: Compressing Large Language Models through Layer Concatenation
Fei Wang, Li Shen, Liang Ding +3
Large Language Models excel at natural language processing tasks, but their massive size leads to high computational and storage demands. Recent works have sought to reduce their m…
ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
Wentao Tan, Qiong Cao, Chao Xue +3
The chart-to-code generation task requires MLLMs to convert chart images into executable code. This task faces two main challenges: limited data diversity and the difficulty of mai…
StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching
Chao Xue, Ziyuan Gao
Text semantic matching requires nuanced understanding of both structural relationships and fine-grained semantic distinctions. While pre-trained language models excel at capturing…