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

I-Fan Lin

ACL 2026SIGIR 2026可解释性对齐与偏好数据与预训练评测与基准检索与知识
新星分新星
79
一作贡献70
核心/参与0
方向广度13
合作网络3
2 一作 · 2 篇 · ACL 2026 / SIGIR 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

2
一作ACL 2026Poster· Natural Language Processing

LLM s Enable Bag-of-Texts Representations for Short-Text Clustering

I-Fan Lin · Faegheh Hasibi · Suzan Verberne

摘要

In this paper, we propose a training-free method for unsupervised short text clustering that relies less on careful selection of embedders than other methods. In customer-facing chatbots, companies are dealing with large amounts of user utterances that need to be clustered according to their inte...

对齐与偏好可解释性数据与预训练OpenReview 项目页
一作SIGIR 2026Poster· Information Retrieval (Short)

LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels

I-Fan Lin · Faegheh Hasibi · Suzan Verberne

摘要

In this paper, we propose an intuitive, training-free and label-free method for intent clustering in conversational search. Current approaches to short text clustering use LLM-generated pseudo-labels to enrich text representations or to identify similar text pairs for pooling. The limitations are...

评测与基准检索与知识可解释性OpenReview

常见合作者