Papers by Neemesh Yadav
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs (2025.coling-main)
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| Challenge: | QUENCH is a text-based English quizzing benchmarking system for large language models (LLMs). |
| Approach: | They propose a text-based English Quizzing Benchmark manually curated from YouTube quiz videos. |
| Outcome: | The proposed system assesses the world knowledge and deduction capabilities of large language models via a zero-shot, open-domain quizzing setup. |
Inference-Time Selective Debiasing to Enhance Fairness in Text Classification Models (2025.naacl-short)
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| Challenge: | Several studies have investigated and promoted fairness, and a variety of definitions have been proposed to address this problem. |
| Approach: | They propose a selective debiasing method that removes bias from model predictions instead of discarding them at inference time. |
| Outcome: | The proposed method achieves better results than standard uncertainty quantification methods on text classification datasets with encoder-based classification models. |
MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models (2026.findings-acl)
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| Challenge: | Existing evaluation frameworks assess isolated responses using coarse-grained taxonomies or static datasets. |
| Approach: | They propose a role-aware mental health safety taxonomy that characterizes clinically significant harm in terms of interactional roles an AI counselor adopts. |
| Outcome: | The proposed framework significantly improves failure-mode coverage and diagnostic granularity. |
Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing (2025.findings-acl)
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| Challenge: | Existing knowledge on how and why NLP methods make content moderation decisions is limited . authors examine how and when to use LLMs in content modeation . |
| Approach: | They use Shapley values and LLM-guided explanations to reverse-engineer content moderation decisions across countries. |
| Outcome: | The proposed methods show that they reverse-engineer content moderation decisions across countries and over time. |