86 citations · 154 across the 84 of their papers we have counts for
37 papers · 1 filter
SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations
Yu Wang, Yuchen Li, Rui Kong +11
Large language models exhibit complementary strengths, motivating routing methods that dispatch each query to the most suitable model. Although existing routers are effective in si…
Measuring Maximum Activations in Open Large Language Models
Luxuan Chen, Han Tian, Xinran Chen +9
The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and m…
KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Shuai Wang, Yinan Yu
Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge-intensive reasoning. Structured Knowledge G…
Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
Shuai Wang, Xixi Wang, Yinan Yu
Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios. Knowledge Base Question An…
MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching
Changle Qu, Sunhao Dai, Hengyi Cai +3
Tool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing rei…
Thinking Forward and Backward: Multi-Objective Reinforcement Learning for Retrieval-Augmented Reasoning
Wenda Wei, Yu-An Liu, Ruqing Zhang +6
Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models, yet its effectiveness remains limited in complex, multi-step…
Learning a Single Token to Replace Long System Prompts in LLMs
Jiancheng Dong, Pengyue Jia, Jingyu Peng +7
Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and consumes valuable context budget. Thi…
Grounding Long-Context Reasoning with Contextual Normalization for Retrieval-Augmented Generation
Jiamin Chen, Yuchen Li, Xinyu Ma +5
Retrieval-Augmented Generation (RAG) has become an essential approach for extending the reasoning and knowledge capacity of large language models (LLMs). While prior research has p…
LLM-Specific Utility for Retrieval-Augmented Generation
Hengran Zhang, Keping Bi, Jiafeng Guo +4
Retrieval-augmented generation (RAG) is typically optimized for topical relevance, yet its success ultimately depends on whether retrieved passages are useful for a large language…
AdaSwitch: Balancing Exploration and Guidance in Knowledge Distillation via Adaptive Switching
Jingyu Peng, Maolin Wang, Hengyi Cai +5
Small language models (SLMs) are crucial for applications with strict latency and computational constraints, yet achieving high performance remains challenging. Knowledge distillat…
Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search
Zeyu Xiong, Yixuan Nan, Li Gao +4
In the dynamic landscape of large-scale web search, Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a give…
A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation
Ziyang Chen, Erxue Min, Xiang Zhao +7
We introduce ChronoQA, a large-scale benchmark dataset for Chinese question answering, specifically designed to evaluate temporal reasoning in Retrieval-Augmented Generation (RAG)…
MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation
Yiqun Chen, Erhan Zhang, Lingyong Yan +4
In question-answering (QA) systems, Retrieval-Augmented Generation (RAG) has become pivotal in enhancing response accuracy and reducing hallucination issues. The architecture of RA…
GRAF: Multi-turn Jailbreaking via Global Refinement and Active Fabrication
Hua Tang, Lingyong Yan, Yukun Zhao +3
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks. Nevertheless, they still pose notable safety risks due to potential misuse for malicious…
Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong +20
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…
Proactive Guidance of Multi-Turn Conversation in Industrial Search
Xiaoyu Li, Xiao Li, Li Gao +5
The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users' interactions…
Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning
Yukun Zhao, Lingyong Yan, Zhenyang Li +4
Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally due to catastrophic forgetting. Existi…
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning
Hang Ni, Fan Liu, Xinyu Ma +5
Large language models (LLMs) have shown promise in automating travel planning, yet they often fall short in addressing nuanced spatiotemporal rationality. While existing benchmarks…
CoRanking: Collaborative Ranking with Small and Large Ranking Agents
Wenhan Liu, Xinyu Ma, Yutao Zhu +4
Large Language Models (LLMs) have demonstrated superior listwise ranking performance. However, their superior performance often relies on large-scale parameters (\eg, GPT-4) and a…
Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models
Zhengliang Shi, Yuhan Wang, Lingyong Yan +4
Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool…
DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models
Zihao Li, Ruixiang Tang, Lu Cheng +3
Pre-trained language models (PLMs) have achieved impressive results on various natural language processing tasks. However, recent research has revealed that these models often rely…
Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking
Junda Zhu, Lingyong Yan, Shuaiqiang Wang +2
Large Reasoning Models (LRMs) have recently demonstrated impressive performances across diverse domains. However, how the safety of Large Language Models (LLMs) benefits from enhan…
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
Yiqun Chen, Lingyong Yan, Weiwei Sun +6
Retrieval-augmented generation (RAG) is widely utilized to incorporate external knowledge into large language models, thereby enhancing factuality and reducing hallucinations in qu…
PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
Jiayi Wu, Hengyi Cai, Lingyong Yan +5
The emergence of Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in the generation of large language models (LLMs), yet it stil…