4 citations · 4 across the 5 of their papers we have counts for
5 papers
Evaluating Large Language Models as Expert Annotators
Yu-Min Tseng, Wei-Lin Chen, Chung-Chi Chen +1
Textual data annotation, the process of labeling or tagging text with relevant information, is typically costly, time-consuming, and labor-intensive. While large language models (L…
SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models
Thinh Pham, Nguyen Nguyen, Pratibha Zunjare +3
We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields conflicting, noisy, or unhelpful re…
Are Expert-Level Language Models Expert-Level Annotators?
Yu-Min Tseng, Wei-Lin Chen, Chung-Chi Chen +1
Data annotation refers to the labeling or tagging of textual data with relevant information. A large body of works have reported positive results on leveraging LLMs as an alternati…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao +4
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., pers…