4 citations · 7 across the 11 of their papers we have counts for
11 papers
UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers
Chidaksh Ravuru, Shashank Srivastava
Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boostin…
Behavioral Coherence: A Method for Sensitive-Domain LLM Evaluation
Anika Sharma, Malavika Mampally, Chidaksh Ravuru +2
People use LLMs for reproductive-health questions, including abortion-related support. A response can sound supportive while answers reinforce harmful assumptions: judgment is like…
Commentary Generation for Soccer Highlights
Chidaksh Ravuru
Automated soccer commentary generation has evolved from template-based systems to advanced neural architectures, aiming to produce real-time descriptions of sports events. While fr…
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani +2
Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we int…
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device techno…
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by…
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Sagar Srinivas Sakhinana, Geethan Sannidhi, Chidaksh Ravuru +1
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynam…
Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models
Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani +2
Characterizing materials using electron micrographs is crucial in areas such as semiconductors and quantum materials. Traditional classification methods falter due to the intricate…
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductor imaging and analysis are critical yet understudied in deep learning, limiting our ability for precise control and optimization in semiconductor manufacturing. We intr…
Agentic Retrieval-Augmented Generation for Time Series Analysis
Chidaksh Ravuru, Sagar Srinivas Sakhinana, Venkataramana Runkana
Time series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal dependencies and distribution shifts in learning from historical…
RESTORE: Graph Embedding Assessment Through Reconstruction
Hong Yung Yip, Chidaksh Ravuru, Neelabha Banerjee +4
Following the success of Word2Vec embeddings, graph embeddings (GEs) have gained substantial traction. GEs are commonly generated and evaluated extrinsically on downstream applicat…