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

Wenqiang Lei

ACL 2026×7推理评测与基准智能体与工具代码可解释性
新星分贡献者
59
一作贡献0
核心/参与30
方向广度10
合作网络9
0 一作 · 7 篇 · ACL 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

7
作者位次 #7/8ACL 2026Poster· Natural Language Processing

Empathy in Diversity: Personalized Depression and Anxiety Therapy via Dialogue State Tracking and Patient-Aware Planning

Xinwei Yang · Junyi Fan · Yuqing Liu · Jiaxuan Wang · Jiashuai Zhang · Hongru Liang · Wenqiang Lei · Yao Song

摘要

Large language model (LLM) dialogue agents are increasingly used in psychological therapy, yet robustness across diverse patients remains underexplored. We address this gap with three contributions: (1) MindEval, a realistic role-play protocol for evaluating therapeutic dialogue agents; (2) MindD...

推理智能体与工具对齐与偏好OpenReview 项目页
作者位次 #5/6ACL 2026Poster· Natural Language Processing

METRO : Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues

Haofu Yang · Jiaji Liu · Chen Huang · Faguo Wu · Wenqiang Lei · See-Kiong Ng

摘要

Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose METRO, a method that leverages large language models to autonomously induce both strategy actions and planning logic directly from raw transcripts. METRO formal...

推理智能体与工具评测与基准OpenReview 项目页
作者位次 #4/5ACL 2026Poster· Natural Language Processing

E 2 ED ev: Benchmarking Large Language Models in End-to-End Software Development Task

Jingyao Liu · Chen Huang · Zhizhao Guan · Wenqiang Lei · Yang Deng

摘要

The rapid advancement in large language models (LLMs) has demonstrated significant potential in End-to-End Software Development (E2ESD). However, existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols, hindering a true understanding...

评测与基准效率与系统代码OpenReview 项目页
作者位次 #5/5ACL 2026Poster· Natural Language Processing

NL \Rightarrow Schedule: Evaluate Multitask Scheduling Capability of Large Language Models

Wenrui Liao · Weihong Du · Yi Li · Hongru Liang · Wenqiang Lei

摘要

Automated schedule generation for multitask from natural language descriptions has huge potential in modern industry. While classic methods bypass language complexities by using pre-formatted matrices, and recent LLM+solver approaches introduce new fragilities by relying on solver-specific code g...

智能体与工具评测与基准推理OpenReview 项目页
作者位次 #6/7ACL 2026Poster· Natural Language Processing

METER : Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models

Pengfeng Li · Chen Huang · Chaoqun Hao · Hongyao Chen · Xiao-Yong Wei · Wenqiang Lei · See-Kiong Ng

摘要

Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. To address this, we pioneer METER to sys...

评测与基准检索与知识推理OpenReview 项目页
作者位次 #5/6ACL 2026Findings· Findings of ACL

Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering

Weikang Zhang · Zimo Zhu · Zhichuan Yang · Chen Huang · Wenqiang Lei · See-Kiong Ng

摘要

Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across varying domains and severity levels. To address this limi...

社会与人本可解释性代码OpenReview 项目页
作者位次 #4/5ACL 2026Findings· Findings of ACL

Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation

Chen Huang · Zitan Jiang · Zou Changyi · Wenqiang Lei · See-Kiong Ng

摘要

Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting high-value information and business intelligence. In response, we make three main contributions. 1) We introduce and define a novel task of Proac...

可解释性代码OpenReview 项目页

常见合作者