76 citations · 89 across the 13 of their papers we have counts for
13 papers
Multi-Objective Bayesian Optimization for Model Merging
Utkarsh Agarwal, Vamshi Bonagiri, Raul Astudillo +1
Model merging combines trained models directly in weight space, offering a compute-efficient alternative to additional fine-tuning. Selecting merge parameters is nevertheless diffi…
Cognitive Digital Twins: Ethical Risks and Governance for AI Systems That Model the Mind
Vamshi Krishna Bonagiri, Juan Nicolas Sepulveda-Arias, Abdoul Jalil Djiberou Mahamadou +1
As AI systems become increasingly persistent and personalized, they make possible a class of technologies that we call cognitive digital twins (CDTs): dynamic computational represe…
Efficient Safety Benchmarking via Item Response Theory
Fabio Spagliardi, Mírian Silva, Ayan Datta +3
Safety benchmarks for language models are typically evaluated using static paradigms that treat all items as equally informative for all models, an assumption that is particularly…
Intrinsic Guardrails: How Semantic Geometry of Personality Interacts with Emergent Misalignment in LLMs
Krishak Aneja, Manas Mittal, Anmol Goel +2
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work lin…
Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety
Vamshi Krishna Bonagiri, Ponnurangam Kumaragurum, Khanh Nguyen +1
As Large Language Model (LLM) agents increasingly operate in complex environments with real-world consequences, their safety becomes critical. While uncertainty quantification is w…
Flying Pigs, FaR and Beyond: Evaluating LLM Reasoning in Counterfactual Worlds
Anish R Joishy, Ishwar B Balappanawar, Vamshi Krishna Bonagiri +3
A fundamental challenge in reasoning is navigating hypothetical, counterfactual worlds where logic may conflict with ingrained knowledge. We investigate this frontier for Large Lan…
From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences
Prashant Kodali, Anmol Goel, Likhith Asapu +5
Current computational approaches for analysing or generating code-mixed sentences do not explicitly model ``naturalness'' or ``acceptability'' of code-mixed sentences, but rely on…
SaGE: Evaluating Moral Consistency in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Priyanshul Govil +2
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally in…
Measuring Moral Inconsistencies in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Manas Gaur +1
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impre…
COBIAS: Assessing the Contextual Reliability of Bias Benchmarks for Language Models
Priyanshul Govil, Hemang Jain, Vamshi Krishna Bonagiri +4
Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices. Current methods for evaluating and mitigating th…
K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries
Kanak Raj, Kaushik Roy, Vamshi Bonagiri +3
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a us…
Towards Effective Paraphrasing for Information Disguise
Anmol Agarwal, Shrey Gupta, Vamshi Bonagiri +3
Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensu…
Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications
Dilrukshi Gamage, Piyush Ghasiya, Vamshi Krishna Bonagiri +2
Deepfakes are synthetic content generated using advanced deep learning and AI technologies. The advancement of technology has created opportunities for anyone to create and share d…