31 citations · 68 across the 42 of their papers we have counts for
8 papers · 1 filter
PrefReward: Learning User Preference Matrix for Personalized Text Generation
Yue Wu, Chengbing Wang, Yimeng Bai +3
Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing person…
Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
Ziyi Zhao, Chongming Gao, Yang Zhang +5
Personalization in Large Language Models (LLMs) often relies on user-specific soft prompts. However, these prompts become obsolete when the foundation model is upgraded, necessitat…
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…
SteerX: Disentangled Steering for LLM Personalization
Xiaoyan Zhao, Ming Yan, Yilun Qiu +5
Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications, including intelligent assistants that support users' daily life a…
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…
Latent Inter-User Difference Modeling for LLM Personalization
Yilun Qiu, Tianhao Shi, Xiaoyan Zhao +3
Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs. Previous work focuses on leveraging a user's…
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…
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning
Ziang Ye, Zhenru Zhang, Yang Zhang +3
When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we arg…