3 citations · 6 across the 7 of their papers we have counts for
7 papers
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs
Mohammad Aflah Khan, Neemesh Yadav, Sarah Masud +1
The rise of large language models (LLMs) has created a need for advanced benchmarking systems beyond traditional setups. To this end, we introduce QUENCH, a novel text-based Englis…
Information Anxiety in Large Language Models
Prasoon Bajpai, Sarah Masud, Tanmoy Chakraborty
Large Language Models (LLMs) have demonstrated strong performance as knowledge repositories, enabling models to understand user queries and generate accurate and context-aware resp…
Independent fact-checking organizations exhibit a departure from political neutrality
Sahajpreet Singh, Sarah Masud, Tanmoy Chakraborty
Independent fact-checking organizations have emerged as the crusaders to debunk fake news. However, they may not always remain neutral, as they can be selective in the false news t…
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech
Neemesh Yadav, Sarah Masud, Vikram Goyal +2
Employing language models to generate explanations for an incoming implicit hate post is an active area of research. The explanation is intended to make explicit the underlying ste…
Probing Critical Learning Dynamics of PLMs for Hate Speech Detection
Sarah Masud, Mohammad Aflah Khan, Vikram Goyal +2
Despite the widespread adoption, there is a lack of research into how various critical aspects of pretrained language models (PLMs) affect their performance in hate speech detectio…
Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection
Sarah Masud, Mohammad Aflah Khan, Md. Shad Akhtar +1
As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it. Reactively, using black-box models to ide…