7 citations · 15 across the 16 of their papers we have counts for
16 papers
DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation
Qiming Zhu, Jialun Cao, Yaojie Lu +4
Code benchmarks such as HumanEval are widely adopted to evaluate the capabilities of Large Language Models (LLMs), providing insights into their strengths and weaknesses. However,…
REInstruct: Building Instruction Data from Unlabeled Corpus
Shu Chen, Xinyan Guan, Yaojie Lu +3
Manually annotating instruction data for large language models is difficult, costly, and hard to scale. Meanwhile, current automatic annotation methods typically rely on distilling…
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation
Boxi Cao, Mengjie Ren, Hongyu Lin +4
Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which s…
Open Grounded Planning: Challenges and Benchmark Construction
Shiguang Guo, Ziliang Deng, Hongyu Lin +3
The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on lev…
Towards Universal Dense Blocking for Entity Resolution
Tianshu Wang, Hongyu Lin, Xianpei Han +3
Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approa…
URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
Zhuoqun Li, Hongyu Lin, Tianshu Wang +7
Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information. Current studies are limited to specific tasks like inf…