8 papers
Prompt and Parameter Co-Optimization for Large Language Models
Xiaohe Bo, Rui Li, Zexu Sun +5
Prompt optimization and fine-tuning are two major approaches to improve the performance of Large Language Models (LLMs). They enhance the capabilities of LLMs from complementary pe…
CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension
Rui Li, Zeyu Zhang, Xiaohe Bo +5
Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive…
Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
Zeyu Zhang, Quanyu Dai, Rui Li +3
LLM-based agents have been extensively applied across various domains, where memory stands out as one of their most essential capabilities. Previous memory mechanisms of LLM-based…
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents
Haoran Tan, Zeyu Zhang, Chen Ma +3
Recent works have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments. However,…
KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
Rui Li, Quanyu Dai, Zeyu Zhang +3
Recent advances in retrieval-augmented generation (RAG) furnish large language models (LLMs) with iterative retrievals of relevant information to handle complex multi-hop questions…
Beyond Single-Point Judgment: Distribution Alignment for LLM-as-a-Judge
Luyu Chen, Zeyu Zhang, Haoran Tan +4
LLMs have emerged as powerful evaluators in the LLM-as-a-Judge paradigm, offering significant efficiency and flexibility compared to human judgments. However, previous methods prim…