Papers by Rohini Srihari
UNIWIZ: A Unified Large Language Model Orchestrated Wizard for Safe Knowledge Grounded Conversations (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made significant progress in integrating safety and knowledge alignment, but excessive focus on safety alignment can lead to unintended hallucinations. |
| Approach: | They propose a "safety-priming" method to generate synthetic safety data and overcome safety bottlenecks. |
| Outcome: | The proposed framework generates synthetic safety data and overcomes safety bottlenecks. |
ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) increasingly power mental-health chatbots . yet the field lacks a scalable, theory-grounded way to decide which model is more effective to deploy. |
| Approach: | They propose a framework that grounds head-to-head comparisons of Emotional-Support LLMs in Hill’s Exploration–Insight–Action counselling model. |
| Outcome: | The proposed framework matches PhD-level annotators in 85% of Exploration, 83% of Insight, and 86% of Action decisions, demonstrating human-level reliability at a fraction of the cost. |
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
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| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
| Approach: | They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection. |
| Outcome: | The proposed model is based on linguistic features and will be extended in the future . it will be used to improve the existing model and improve the tools in the field of fake news detection . |
ArgU: A Controllable Factual Argument Generator (2023.acl-long)
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| Challenge: | Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. |
| Approach: | They propose a controllable neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure. |
| Outcome: | The proposed model produces factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. |
Diving Deep into Modes of Fact Hallucinations in Dialogue Systems (2022.findings-emnlp)
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| Challenge: | Knowledge Graph(KG) grounded conversations often use large pre-trained models and suffer from fact hallucination. |
| Approach: | They propose to use a human feedback analysis to identify various modes of hallucination in KG chatbots. |
| Outcome: | The proposed system provides fine-grained signals that control fallacious content while generating responses. |
Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (2024.emnlp-main)
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| Challenge: | scalable strategies to combat online misinformation are short-term and insufficient, authors say . current reactive approaches, like content flagging and banning, do little to change perception of misinformants . human evaluations show that our framework generates expert-like responses . |
| Approach: | They propose a framework that generates persuasive responses from hate-speech counter-responses . human evaluations show that the framework generates expert-like responses . |
| Outcome: | The proposed framework generates expert-like responses and is 14% more engaging, 21% more natural, and 18% more factual than the best available alternatives. |