paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
CW

Chengbing Wang

SIGIR 2026×2ACL 2026ICLR 2026对齐与偏好评测与基准检索与知识推理强化学习与后训练
新星分新星
85
一作贡献70
核心/参与10
方向广度16
合作网络9
2 一作 · 4 篇 · SIGIR 2026 / ACL 2026 / ICLR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

4
一作ACL 2026Findings· Findings of ACL

PERM : Psychology-grounded Empathetic Reward Modeling for Large Language Models

Chengbing Wang · Wuqiang Zheng · Yang Zhang · Fengbin ZHU · Junyi Cheng · Yi Xie · Wenjie Wang · Fuli Feng

摘要

Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL) has been utilized to enhance empathy of LLMs, existing reward models typically evaluate empathy from a single persp...

对齐与偏好强化学习与后训练评测与基准OpenReview 项目页
作者位次 #3/11SIGIR 2026Poster· Information Retrieval (Resource)

AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment

Jianfei Xiao · Xiang Yu · Chengbing Wang · Wuqiang Zheng · Xinyu Lin · Kaining Liu · Hongxun Ding · Yang Zhang · Wenjie Wang · Fuli Feng · Xiangnan He

摘要

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 of a gold-standard evaluation benchmark. Existing benchmarks either overlook personalized information management that...

对齐与偏好检索与知识评测与基准OpenReview

常见合作者