1 citations · 1 across the 8 of their papers we have counts for
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MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models
Suhyun Lee, Palakorn Achananuparp, Neemesh Yadav +2
Large language models (LLMs) are increasingly explored as scalable tools for mental health counseling, yet evaluating their safety remains challenging due to the interactional and…
DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories
Neemesh Yadav, Palakorn Achananuparp, Jing Jiang +1
We introduce DialToM, an annotated Theory of Mind (ToM) benchmark built from naturalistic human-human dialogues using a multiple-choice evaluation framework. Concurrent with recent…
Are LLMs Good Safety Agents or a Propaganda Engine?
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a reflection of its safety policies…
Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games
Neemesh Yadav, Yihuai Lan, Shan Dong +4
Large Language Models (LLMs) have shown potential in simulating human behaviors and performing theory-of-mind (ToM) reasoning, crucial for complex social interactions. We investiga…
Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing
Neemesh Yadav, Jiarui Liu, Francesco Ortu +3
The ability of Natural Language Processing (NLP) methods to categorize text into multiple classes has motivated their use in online content moderation tasks, such as hate speech an…
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…