3 papers
cs.CL2025
Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction
Saurabh Srivastava, Ziyu Yao
Large Reasoning Models (LRMs) such as DeepSeek-R1 and OpenAI o1 have demonstrated remarkable capabilities in various reasoning tasks. Their strong capability to generate and reason…
cs.CL2025
Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines
Saurabh Srivastava, Sweta Pati, Ziyu Yao
In this work, we study the effect of annotation guidelines -- textual descriptions of event types and arguments, when instruction-tuning large language models for event extraction.…
cs.CL2024
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance
Saurabh Srivastava, Chengyue Huang, Weiguo Fan +1
Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. Despite its ad…