1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.CL2026★ 1 cited
Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
Yuling Shi, Maolin Sun, Zijun Liu +4
Retrieval-Augmented Generation (RAG) has demonstrated significant effectiveness in enhancing large language models (LLMs) for complex multi-hop question answering (QA). For multi-h…
cs.CL2025
LastingBench: Defend Benchmarks Against Knowledge Leakage
Yixiong Fang, Tianran Sun, Yuling Shi +2
The increasing complexity of large language models (LLMs) raises concerns about their ability to "cheat" on standard Question Answering (QA) benchmarks by memorizing task-specific…
cs.CL2025
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation
Yixiong Fang, Tianran Sun, Yuling Shi +1
While RAG demonstrates remarkable capabilities in LLM applications, its effectiveness is hindered by the ever-increasing length of retrieved contexts, which introduces information…