1 citations · 1 across the 5 of their papers we have counts for
5 papers
MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining
Haoyu Dong, Pengkun Zhang, Mingzhe Lu +2
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machin…
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval
Yanzhen Shen, Sihao Chen, Xueqiang Xu +3
While significant progress has been made with dual- and bi-encoder dense retrievers, they often struggle on queries with logical connectives, a use case that is often overlooked ye…
GraphRouter: A Graph-based Router for LLM Selections
Tao Feng, Yanzhen Shen, Jiaxuan You
The rapidly growing number and variety of Large Language Models (LLMs) present significant challenges in efficiently selecting the appropriate LLM for a given query, especially con…
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
Yu Zhang, Yunyi Zhang, Yanzhen Shen +5
Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-anno…
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion
Yanzhen Shen, Yu Zhang, Yunyi Zhang +1
Entity set expansion, taxonomy expansion, and seed-guided taxonomy construction are three representative tasks that can be applied to automatically populate an existing taxonomy wi…