3 citations · 6 across the 12 of their papers we have counts for
13 papers
Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain
Léo Boisvert, Abhay Puri, Chandra Kiran Reddy Evuru +7
While finetuning AI agents on interaction data -- such as web browsing or tool use -- improves their capabilities, it also introduces critical security vulnerabilities within the a…
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
Leo Boisvert, Mihir Bansal, Chandra Kiran Reddy Evuru +9
We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework and integrates easily into realistic ag…
ProSE: Diffusion Priors for Speech Enhancement
Sonal Kumar, Sreyan Ghosh, Utkarsh Tyagi +4
Speech enhancement (SE) is the foundational task of enhancing the clarity and quality of speech in the presence of non-stationary additive noise. While deterministic deep learning…
ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds
Sreyan Ghosh, Sonal Kumar, Chandra Kiran Reddy Evuru +3
Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categori…
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
Sreyan Ghosh, Sonal Kumar, Ashish Seth +6
Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a…
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions
Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +4
We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a…
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +4
Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations. While hallucinations are well-studied, th…
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
Chandra Kiran Reddy Evuru, Sreyan Ghosh, Sonal Kumar +3
We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP. Our a…
A Closer Look at the Limitations of Instruction Tuning
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +5
Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trai…
DALE: Generative Data Augmentation for Low-Resource Legal NLP
Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar +4
We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effec…
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
Sreyan Ghosh, Ashish Seth, Sonal Kumar +7
A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representatio…
RECAP: Retrieval-Augmented Audio Captioning
Sreyan Ghosh, Sonal Kumar, Chandra Kiran Reddy Evuru +2
We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and effective audio captioning system that generates captions conditioned on an input audio and other captions simi…
ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +4
Neural image classifiers can often learn to make predictions by overly relying on non-predictive features that are spuriously correlated with the class labels in the training data.…