6 papers · 1 filter
AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents
Bohan Yu, Pengfei Cao, Kai Tian +8
Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural knowledge about how to act. Exist…
RuleWeaver: Benchmarking Rule-Centered Scenario Reasoning for Large Language Models
Bohan Yu, Shi-Yang Li, Pengfei Cao +2
Large language models (LLMs) are increasingly applied to specialized domains, where effective use of domain expertise often requires reasoning over complex rules in concrete scenar…
Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models
Bohan Yu, Pengfei Cao, Chen Han +7
Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided procedural rules at scale. To evalu…
PReM: Learning What to Preserve and When to Refresh for Context Compression
Bohan Yu, Lei Shen, Chenxi Zhou +5
Efficient long-context inference is not only about reducing memory cost, but also about keeping useful contextual evidence accessible as generation proceeds. However, existing comp…
SR-KI: Scalable and Real-Time Knowledge Integration into LLMs via Supervised Attention
Bohan Yu, Wei Huang, Kang Liu
This paper proposes SR-KI, a novel approach for integrating real-time and large-scale structured knowledge bases (KBs) into large language models (LLMs). SR-KI begins by encoding K…
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering
Junnan Zhu, Jingyi Wang, Bohan Yu +4
LLMs have shown impressive progress in natural language processing. However, they still face significant challenges in TableQA, where real-world complexities such as diverse table…