Papers by Anand Kumar
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
Copied to clipboard
Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding (2025.emnlp-main)
Copied to clipboard
Ashish Seth, Utkarsh Tyagi, Ramaneswaran Selvakumar, Nishit Anand, Sonal Kumar, Sreyan Ghosh, Ramani Duraiswami, Chirag Agarwal, Dinesh Manocha
| Challenge: | Multimodal Large Language Models excel at visual perception and reasoning in third-person and egocentric videos, but are prone to hallucinations, generating coherent yet inaccurate responses. |
| Approach: | They propose to use a benchmark to evaluate MLLM hallucinations in egocentric videos. |
| Outcome: | EGOILLUSION comprises 1,400 videos paired with 8,000 human-annotated open and closed-ended questions designed to trigger hallucinations in both visual and auditory cues in egocentric videos. |
MULTIVOX: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions (2025.emnlp-main)
Copied to clipboard
Ramaneswaran Selvakumar, Ashish Seth, Nishit Anand, Utkarsh Tyagi, Sonal Kumar, Sreyan Ghosh, Dinesh Manocha
| Challenge: | omni models lack spoken dialogues, which is essential for assessing conversational and auditory capabilities of voice assistants. |
| Approach: | They propose a benchmark to evaluate the ability of voice assistants to integrate paralinguistic speech features into their models. |
| Outcome: | The multivox voice assistant benchmark evaluates the ability of models to integrate spoken and visual cues including paralinguistic speech features for truly multimodal understanding. |
Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Small shifts in user behavior can cause sharp drops in agent performance . prior work has shown that LLMs lack robustness to real-world noise and small input perturbations. |
| Approach: | They propose a model-agnostic method for systematically stress testing AI agents that learns directions in activation space corresponding to steerable user traits. |
| Outcome: | The proposed method can be used to stress test AI agents in airline, retail, telecom, and telehealth domains. |
Do Audio-Language Models Understand Linguistic Variations? (2025.naacl-short)
Copied to clipboard
Ramaneswaran Selvakumar, Sonal Kumar, Hemant Kumar Giri, Nishit Anand, Ashish Seth, Sreyan Ghosh, Dinesh Manocha
| Challenge: | Existing open-vocabulary audio language models struggle to generalize to linguistic variations in textual queries. |
| Approach: | They propose a novel technique to learn audio-language representations agnostic to linguistic variations by reformulating contrastive loss used in CLAP architectures. |
| Outcome: | The proposed approach improves the performance of the open-vocabulary audio language models by 0.8%-13% across benchmarks and enhances robustness to linguistic variation. |