16 citations · 38 across the 18 of their papers we have counts for
20 papers
PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents
Huayi Lai, Shichao Song, Qingchen Yu +4
Large language model (LLM) agents are evolving from tool-calling systems that execute isolated instructions into task-oriented agents that pursue user goals through sustained, mult…
UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning
Xiaolong Wei, Zerun Zhu, Simin Niu +9
A fundamental challenge in creative writing lies in reconciling the inherent tension between maintaining global coherence in long-form narratives and preserving local expressivenes…
HaluMem: Evaluating Hallucinations in Memory Systems of Agents
Ding Chen, Simin Niu, Kehang Li +6
Memory systems are key components that enable AI systems such as LLMs and AI agents to achieve long-term learning and sustained interaction. However, during memory storage and retr…
MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems
Jihao Zhao, Zhiyuan Ji, Simin Niu +3
The traditional RAG paradigm, which typically engages in the comprehension of relevant text chunks in response to received queries, inherently restricts both the depth of knowledge…
MemOS: A Memory OS for AI System
Zhiyu Li, Chenyang Xi, Chunyu Li +36
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…
GuessArena: Guess Who I Am? A Self-Adaptive Framework for Evaluating LLMs in Domain-Specific Knowledge and Reasoning
Qingchen Yu, Zifan Zheng, Ding Chen +4
The evaluation of large language models (LLMs) has traditionally relied on static benchmarks, a paradigm that poses two major limitations: (1) predefined test sets lack adaptabilit…
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
Zhiyu Li, Shichao Song, Hanyu Wang +19
Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language…
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models
Xun Liang, Hanyu Wang, Huayi Lai +7
Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottlene…
MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System
Jihao Zhao, Zhiyuan Ji, Zhaoxin Fan +5
Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline.…
SurveyX: Academic Survey Automation via Large Language Models
Xun Liang, Jiawei Yang, Yezhaohui Wang +11
Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated sur…
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model
Xun Liang, Simin Niu, Zhiyu Li +8
The indexing-retrieval-generation paradigm of retrieval-augmented generation (RAG) has been highly successful in solving knowledge-intensive tasks by integrating external knowledge…
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
Qingchen Yu, Shichao Song, Ke Fang +5
As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, mak…
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception
Jihao Zhao, Zhiyuan Ji, Yuchen Feng +5
While Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for boosting large language models (LLMs) in knowledge-intensive tasks, it often overlooks the crucia…
QAEncoder: Towards Aligned Representation Learning in Question Answering Systems
Zhengren Wang, Qinhan Yu, Shida Wei +6
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders…
Controllable Text Generation for Large Language Models: A Survey
Xun Liang, Hanyu Wang, Yezhaohui Wang +8
In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly…
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability
Yanfang Chen, Ding Chen, Shichao Song +5
As people increasingly prioritize their health, the speed and breadth of health information dissemination on the internet have also grown. At the same time, the presence of false h…
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning
Xun Liang, Simin Niu, Zhiyu li +7
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs). Nevertheless, constructing and validating…
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
Xiaomin Yu, Yezhaohui Wang, Yanfang Chen +5
In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods.…
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
Yuanjie Lyu, Zhiyu Li, Simin Niu +7
Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresse…
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
Xun Liang, Shichao Song, Simin Niu +8
Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingly being applied across a diverse range of industrie…