5 papers
Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory
Han Zhang, Zihao Tang, Xin Yu +8
In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the underlying personas tend to be fl…
Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory
Zihao Tang, Xin Yu, Ziyu Xiao +9
AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet existing methods (RAG and Graph-R…
LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrieval
Wenchang Lei, Ping Zou, Yue Wang +2
Large language models (LLMs) exhibit strong semantic understanding, yet struggle when user instructions involve ambiguous or conceptually misaligned terms. We propose the Language…
Large Language Models are overconfident and amplify human bias
Fengfei Sun, Ningke Li, Kailong Wang +1
Large language models (LLMs) are revolutionizing every aspect of society. They are increasingly used in problem-solving tasks to substitute human assessment and reasoning. LLMs are…
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
Yaming Yang, Dilxat Muhtar, Yelong Shen +9
Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. Ho…