21 citations · 24 across the 4 of their papers we have counts for
4 papers
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…
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.…
Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap
Saurabh Srivastava, Annarose M B, Anto P +6
We propose a framework for robust evaluation of reasoning capabilities of language models, using functional variants of benchmarks. Models that solve a reasoning test should exhibi…
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…