Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies

1 code implementation • 6 Jun 2024 • Aswin RRV, Nemika Tyagi, Md Nayem Uddin, Neeraj Varshney, Chitta Baral

This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not entirely correct.

Hallucination Knowledge Probing +1

Asking and Answering Questions to Extract Event-Argument Structures

1 code implementation • 25 Apr 2024 • Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, Steven Corman

Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer.

coreference-resolution Data Augmentation +2

Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction

no code implementations • 7 Apr 2024 • Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, Steven Corman

This paper presents multiple question generation strategies for document-level event argument extraction.

Event Argument Extraction Question Generation +1

Interpreting Indirect Answers to Yes-No Questions in Multiple Languages

1 code implementation • 20 Oct 2023 • Zijie Wang, Md Mosharaf Hossain, Shivam Mathur, Terry Cruz Melo, Kadir Bulut Ozler, Keun Hee Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Nupur Shakya, Md Nayem Uddin, Eduardo Blanco

Experimental results demonstrate that monolingual fine-tuning is beneficial if training data can be obtained via distant supervision for the language of interest (5 languages).