5 papers
How Reliable are LLMs for Reasoning on the Re-ranking task?
Nafis Tanveer Islam, Zhiming Zhao
With the improving semantic understanding capability of Large Language Models (LLMs), they exhibit a greater awareness and alignment with human values, but this comes at the cost o…
Helix: A Dual-Helix Co-Evolutionary Multi-Agent System for Prompt Optimization and Question Reformulation
Kewen Zhu, Liping Yi, Zhiming Zhao +2
Automated prompt optimization (APO) aims to improve large language model performance by refining prompt instructions. However, existing methods are largely constrained by fixed pro…
Social Hippocampus Memory Learning
Liping Yi, Zhiming Zhao, Qinghua Hu
Social learning highlights that learning agents improve not in isolation, but through interaction and structured knowledge exchange with others. When introduced into machine learni…
FedPDPO: Federated Personalized Direct Preference Optimization for Large Language Model Alignment
Kewen Zhu, Liping Yi, Zhiming Zhao +3
Aligning large language models (LLMs) with human preferences in federated learning (FL) is challenging due to decentralized, privacy-sensitive, and highly non-IID preference data.…
How good are LLMs at Retrieving Documents in a Specific Domain?
Nafis Tanveer Islam, Zhiming Zhao
Classical search engines using indexing methods in data infrastructures primarily allow keyword-based queries to retrieve content. While these indexing-based methods are highly sca…