79 citations · 447 across the 119 of their papers we have counts for
132 papers
DirectSpeech2LLM: A Simple End-to-End Framework to Mitigate Prompt Overfitting in Speech-LLMs
Hemant Yadav, Sunayana Sitaram, Roger Zimmermann +1
Speech-LLMs often exhibit prompt overfitting, where models solely trained on automatic speech recognition (ASR) instruction fail to generalize to new instructions such as speech tr…
dKFD: Phase-Structured Evidence Allocation for Fixed-Budget Localized Event Understanding
Aditya Bagri, Ashutosh Kumar, Chaitanya Lakhchaura +3
Sparse video understanding often requires selecting a small set of visual evidence under a fixed frame budget. Most sparse selectors allocate this budget globally, allowing all fra…
NutriVision: Ingredient-Conditioned Fusion and Prediction for Single-Image Food Nutrition Estimation
Aman Kumar, Avinash Anand, Chaitanya Lakhchaura +5
Nutrition estimation is a fundamental task in consumer diet tracking, clinical dietetics, chronic disease management, sports and hospital nutrition, and broader food computing syst…
Exploring Collaboration between a language and a non-language agent
Harini S, Somesh Singh, Yaman K Singla +3
LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in many important domains like game…
TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention
Avni Mittal, Avinash Anand, Ashutosh Kumar +7
Can a strictly local, iterated, weight-shared computation primitive support language modelling, and which of those three properties actually drives the model's behaviour? We define…
StocksTalk: A Voice-Enabled Conversational Agent for Structured Query Generation over Web Data
Akshat Parmar, Vikranth Udandarao, Abhay Shakya +4
StocksTalk is a voice-enabled conversational system for transforming spoken financial screening requests into executable and validated structured queries over real-world market dat…
InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah +1
Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential Fir…
Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models
Raj Jaiswal, Dhruv Jain, Rishabh Dhawan +4
Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited domain knowledge, hallucina…
Obey, Diverge, Collapse: Blind Obedience to Incorrect Instructions Drives Code LLMs to Irrecoverable Code Semantic Collapse
Raj Jaiswal, Anany Singh Divy, Savar Bhasin +3
Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the ins…
ZIPP:Zero-shot Image Personalization from Personas
Harini SI, Somesh Singh, Yaman Kumar Singla +2
Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than indivi…
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
Varun Khurana, Vijval Ekbote, Vashu Chauhan +3
In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features d…
The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
Avinash Anand, Mahisha Ramesh, Avni Mittal +8
Reasoning has become central to how Large Language Models (LLMs) are evaluated and interpreted, spanning Chain-of-Thought (CoT), mathematical problem-solving, multi-hop question an…
How Well Do Large Language Models Capture Human Personality?
Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah +2
Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavio…
IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning
Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand +8
Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consistent step-by-step reasoning, e…
Accelerating Social Science Research via Agentic Hypothesization and Experimentation
Jishu Sen Gupta, Harini SI, Somesh Kumar Singh +5
Data-driven social science research is inherently slow, relying on iterative cycles of observation, hypothesis generation, and experimental validation. While recent data-driven met…
LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering
Aarya Khandelwal, Ritwik Mishra, Rajiv Ratn Shah
Long-context question answering (QA) over literary texts poses significant challenges for modern large language models, particularly in low-resource languages. We address the scarc…
Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data
Mann Khatri, Mirza Yusuf, Rajiv Ratn Shah +1
Large Language Models (LLMs), trained on extensive datasets from the web, exhibit remarkable general reasoning skills. Despite this, they often struggle in specialized areas like l…
Detecting LLM-Assisted Academic Dishonesty using Keystroke Dynamics
Atharva Mehta, Rajesh Kumar, Aman Singla +3
The rapid adoption of generative AI tools has heightened concerns regarding academic integrity, as students increasingly engage in dishonest practices by copying or paraphrasing AI…
On Optimal Steering to Achieve Exact Fairness
Mohit Sharma, Amit Jayant Deshpande, Chiranjib Bhattacharyya +1
To fix the 'bias in, bias out' problem in fair machine learning, it is important to steer feature distributions of data or internal representations of Large Language Models (LLMs)…
Enhancing Scientific Visual Question Answering via Vision-Caption aware Supervised Fine-Tuning
Janak Kapuriya, Anwar Shaikh, Arnav Goel +8
In this study, we introduce Vision-Caption aware Supervised FineTuning (VCASFT), a novel learning paradigm designed to enhance the performance of smaller Vision Language Models(VLM…
Spiritual-LLM : Gita Inspired Mental Health Therapy In the Era of LLMs
Janak Kapuriya, Aman Singh, Jainendra Shukla +1
Traditional mental health support systems often generate responses based solely on the user's current emotion and situations, resulting in superficial interventions that fail to ad…
REWIND: Speech Time Reversal for Enhancing Speaker Representations in Diffusion-based Voice Conversion
Ishan D. Biyani, Nirmesh J. Shah, Ashishkumar P. Gudmalwar +2
Speech time reversal refers to the process of reversing the entire speech signal in time, causing it to play backward. Such signals are completely unintelligible since the fundamen…
Med-CoDE: Medical Critique based Disagreement Evaluation Framework
Mohit Gupta, Akiko Aizawa, Rajiv Ratn Shah
The emergence of large language models (LLMs) has significantly influenced numerous fields, including healthcare, by enhancing the capabilities of automated systems to process and…
Long-context Non-factoid Question Answering in Indic Languages
Ritwik Mishra, Rajiv Ratn Shah, Ponnurangam Kumaraguru
Question Answering (QA) tasks, which involve extracting answers from a given context, are relatively straightforward for modern Large Language Models (LLMs) when the context is sho…