12 citations · 26 across the 15 of their papers we have counts for
16 papers
Geometry of Decision Making in Language Models
Abhinav Joshi, Divyanshu Bhatt, Ashutosh Modi
Large Language Models (LLMs) show strong generalization across diverse tasks, yet the internal decision-making processes behind their predictions remain opaque. In this work, we st…
Beyond Components: Singular Vector-Based Interpretability of Transformer Circuits
Areeb Ahmad, Abhinav Joshi, Ashutosh Modi
Transformer-based language models exhibit complex and distributed behavior, yet their internal computations remain poorly understood. Existing mechanistic interpretability methods…
Calibration Across Layers: Understanding Calibration Evolution in LLMs
Abhinav Joshi, Areeb Ahmad, Ashutosh Modi
Large Language Models (LLMs) have demonstrated inherent calibration capabilities, where predicted probabilities align well with correctness, despite prior findings that deep neural…
POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation
Abhinav Joshi, Vaibhav Sharma, Sanjeet Singh +1
Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and archit…
Towards Quantifying Commonsense Reasoning with Mechanistic Insights
Abhinav Joshi, Areeb Ahmad, Divyaksh Shukla +1
Commonsense reasoning deals with the implicit knowledge that is well understood by humans and typically acquired via interactions with the world. In recent times, commonsense reaso…
COLD: Causal reasOning in cLosed Daily activities
Abhinav Joshi, Areeb Ahmad, Ashutosh Modi
Large Language Models (LLMs) have shown state-of-the-art performance in a variety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of L…
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Abhinav Joshi, Shaswati Saha, Divyaksh Shukla +4
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vas…
iSign: A Benchmark for Indian Sign Language Processing
Abhinav Joshi, Romit Mohanty, Mounika Kanakanti +4
Indian Sign Language has limited resources for developing machine learning and data-driven approaches for automated language processing. Though text/audio-based language processing…
IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning
Abhinav Joshi, Shounak Paul, Akshat Sharma +3
Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and un…
ISLTranslate: Dataset for Translating Indian Sign Language
Abhinav Joshi, Susmit Agrawal, Ashutosh Modi
Sign languages are the primary means of communication for many hard-of-hearing people worldwide. Recently, to bridge the communication gap between the hard-of-hearing community and…
U-CREAT: Unsupervised Case Retrieval using Events extrAcTion
Abhinav Joshi, Akshat Sharma, Sai Kiran Tanikella +1
The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To furthe…
ScriptWorld: Text Based Environment For Learning Procedural Knowledge
Abhinav Joshi, Areeb Ahmad, Umang Pandey +1
Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents. Existing text-b…
SemEval 2023 Task 6: LegalEval - Understanding Legal Texts
Ashutosh Modi, Prathamesh Kalamkar, Saurabh Karn +6
In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal…
Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments
Abhinav Joshi, Naman Gupta, Jinang Shah +3
A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and…
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
Abhinav Joshi, Ashwani Bhat, Ayush Jain +2
Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation in…
Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts
Harsh Agarwal, Keshav Bansal, Abhinav Joshi +1
Emotion Recognition in Conversations (ERC) is an important and active research area. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video)…