Papers by Ashish Sharma
Investigating Agency of LLMs in Human-AI Collaboration Tasks (2024.eacl-long)
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| Challenge: | We examine how LLMs can be measured and managed for Agency . a model that manifests high Intentionality, Motivation, Self-Efficacy, and Self-Regulation is more likely to be perceived as strongly agentive. |
| Approach: | They collect a dataset of 83 human-human collaborative interior design conversations containing 908 conversational snippets annotated for Agency features. |
| Outcome: | The proposed models show that they manifest high Intentionality, Motivation, Self-Efficacy, and Self-Regulation, and are more likely to be perceived as agentive. |
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction (2023.acl-long)
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Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, Tim Althoff
| Challenge: | Psychotherapy can help people overcome negative thoughts by replacing them with a more hopeful "reframed thought" but clinician shortages and mental health stigma often limit access to therapy. |
| Approach: | They propose a framework of seven linguistic attributes that can be used to reframe a thought . they use a retrieval-enhanced in-context learning model to generate reframed thoughts . |
| Outcome: | The proposed model is based on a human-centered study of 600 situations, thoughts and reframes on 2,000 mental health websites. |
Power doesn’t reside in size: A Low Parameter Hybrid Language Model (HLM) for Sentiment Analysis in Code-mixed data (2025.emnlp-main)
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Pavan Sai Balaga, Nagasamudram Karthik, Challa Vishwanath, Raksha Sharma, Rudra Murthy, Ashish Mittal
| Challenge: | Code-mixed text presents significant challenges for machine learning due to interplay of distinct grammatical structures, effectively forming a hybrid language. |
| Approach: | They propose a Hybrid Language Model that combines a multilingual encoder and a lightweight decoder to achieve sentiment classification performance comparable to those of fine-tuned Large Language Models. |
| Outcome: | The proposed model outperforms models trained individually in sentiment detection tasks. |
ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into agentic frameworks to assist individual users in completing diverse tasks. |
| Approach: | They propose a simulation environment with a plug-and-play proactive AI mediator . they use a socio-cognitive evaluation framework to measure consensus changes, intervention latency, mediator effectiveness and intelligence. |
| Outcome: | The proposed model outperforms a generic baseline in multi-party negotiation scenarios while being 77% faster in response. |
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020.emnlp-main)
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| Challenge: | Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. |
| Approach: | They propose a computational approach to understanding how empathy is expressed in online mental health platforms. |
| Outcome: | The proposed model can identify empathic conversations and extract rationales from them. |
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction (2024.acl-long)
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| Challenge: | Various communication frameworks assist individuals in conducting difficult conversations by providing a set of skills to apply. |
| Approach: | They propose a language-based simulation system that provides just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. |
| Outcome: | The proposed training system improves self-efficacy and reduces negative emotions by 27% compared to the GPT-4 training system. |
Gendered Mental Health Stigma in Masked Language Models (2022.emnlp-main)
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Inna Lin, Lucille Njoo, Anjalie Field, Ashish Sharma, Katharina Reinecke, Tim Althoff, Yulia Tsvetkov
| Challenge: | Mental health stigma prevents many individuals from receiving appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. |
| Approach: | They propose to use clinical psychology literature to curate prompts, then evaluate models’ propensity to generate gendered words. |
| Outcome: | The proposed framework captures stigma about gender in mental health and is more likely to predict female subjects than male in sentences about mental health conditions (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. |