42 citations · 127 across the 41 of their papers we have counts for
6 papers · 1 filter
Towards Root Memories: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs
Hongxun Ding, Xiang Yu, Chengbing Wang +4
Memory systems are essential for personalized Large Language Models (LLMs). However, existing retrieval methods in these systems primarily rely on semantic similarity, potentially…
AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment
Jianfei Xiao, Xiang Yu, Chengbing Wang +8
As Large Language Models (LLMs) evolve into lifelong AI assistants, LLM personalization has become a critical frontier. However, progress is currently bottlenecked by the absence o…
Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation
Chengbing Wang, Yang Zhang, Wenjie Wang +4
Preference alignment has enabled large language models (LLMs) to better reflect human expectations, but current methods mostly optimize for population-level preferences, overlookin…
Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code
Keqin Bao, Nuo Chen, Xiaoyuan Li +5
Enhancing reasoning capabilities remains a central focus in the LLM reasearch community. A promising direction involves requiring models to simulate code execution step-by-step to…
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search
Wentao Shi, Zichun Yu, Fuli Feng +2
Monte Carlo Tree Search (MCTS) based methods provide promising approaches for generating synthetic data to enhance the self-training of Large Language Model (LLM) based multi-agent…
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Boyi Deng, Wenjie Wang, Fuli Feng +3
Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or au…