Papers by Ashish Kumar
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding (2025.emnlp-main)
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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. |
Semantic alignment in hyperbolic space for fine-grained emotion classification (2025.acl-srw)
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| Challenge: | Existing approaches to fine-grained emotion classification operate in Euclidean space, where the flat geometry makes it difficult to distinguish semantically similar label labels. |
| Approach: | They propose a semantic alignment framework that leverages the Lorentz model of hyperbolic space to embed text and label representations into hyperbolical space via the exponential map. |
| Outcome: | The proposed framework improves on two benchmark FEC datasets. |
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities (2024.emnlp-main)
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Sreyan Ghosh, Sonal Kumar, Ashish Seth, Chandra Kiran Evuru, Utkarsh Tyagi, S Sakshi, Oriol Nieto, Ramani Duraiswami, Dinesh Manocha
| Challenge: | We propose a novel large-scale audio-language model with advanced audio understanding and reasoning abilities. |
| Approach: | They propose a general-purpose large audio-language model with advanced audio understanding and reasoning abilities that integrates an LLM with multiple types of audio representations. |
| Outcome: | The proposed model outperforms existing models on audio understanding tasks by 1%-84%. |
HyILR: Hyperbolic Instance-Specific Local Relationships for Hierarchical Text Classification (2025.acl-srw)
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| Challenge: | Hierarchical text classification models rely on capturing global label hierarchy, which contains static and redundant relationships. |
| Approach: | They propose a method which captures hierarchical relationships without encoding global hierarchy . they use hyperbolic geometry to model instance-specific local relationships using Lorentz model . |
| Outcome: | The proposed model captures hierarchical relationships without encoding global hierarchy . the proposed model is superior to baseline methods on four benchmark datasets . |
CEBC: Conformal Evidence-Bounded Control for Low-Hallucination Vision–Language Generation (2026.acl-long)
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| Challenge: | Existing mitigation approaches reduce hallucinated object mentions at the cost of degraded generation quality or require expensive retraining and task-specific supervision. |
| Approach: | They propose a lightweight framework for low-hallucination vision–language generation . it uses evidence-bounded minimal editing to revise or suppress unsupported referenced entities . |
| Outcome: | The proposed framework reduces hallucinations while maintaining or improving quality metrics. |
PAT: Parameter-Free Audio-Text Aligner to Boost Zero-Shot Audio Classification (2025.naacl-long)
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| Challenge: | Audio-Language Models (ALMs) have demonstrated remarkable performance in zero-shot audio classification. |
| Approach: | They propose a training-free method that enhances audio and language representations using mutual feedback. |
| Outcome: | The proposed method outperforms vanilla zero-shot evaluation with significant margins of 0.42%-27.0%. |
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI (2021.emnlp-main)
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| Challenge: | a new reasoning challenge is proposed to help AI systems to solve real-world problems . Fermi Problems are questions whose answers can only be approximated because their computation is either impossible or impossible. |
| Approach: | They propose a new reasoning challenge, Fermi Problems, which asks questions whose answers can only be approximated because their computation is either impractical or impossible. |
| Outcome: | The proposed datasets show that even fine-tuned large-scale language models perform poorly on these datasets. |
ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation (2026.findings-acl)
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Prince Kumar, Vitobha Munigala, Jaydeep Sen, Ashish Mittal, Vishwajeet Kumar, Srikanth G. Tamilselvam
| Challenge: | Existing methods for code explanations fail to distinguish correct from partially or fully incorrect explanations and their similarity scores are poorly calibrated. |
| Approach: | They propose a model-agnostic graded fine-tuning framework that learns calibrated similarity representations between code and explanations to support fine-grained supervision and evaluation. |
| Outcome: | The proposed framework improves F1 score and ECE scores on two embedding models and reduces expected calibration error. |
TEN: Table Explicitization, Neurosymbolically (2026.acl-industry)
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| Challenge: | Existing methods for extracting tabular data from semistructured text are error-prone and costly. |
| Approach: | They propose a neurosymbolic approach to extract tabular data from semistructured text . TEN is a triadic feedback loop that iteratively refines table hypotheses . |
| Outcome: | The proposed approach outperforms neural baselines in exact match accuracy and lower hallucination rates. |
MULTIVOX: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions (2025.emnlp-main)
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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. |
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning (2024.emnlp-main)
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| Challenge: | EH-MAM is a self-supervised learning approach for speech representation learning . prior methods used random masking schemes to learn speech representations . |
| Approach: | They propose a self-supervised approach that automatically selects hard regions during SSL training and introduces them to the model for reconstruction. |
| Outcome: | The proposed approach outperforms state-of-the-art models across low-resource speech recognition and SUPERB benchmarks by 5%-10%. |
Do Audio-Language Models Understand Linguistic Variations? (2025.naacl-short)
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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. |
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages (2025.findings-naacl)
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Abhishek Kumar Singh, Vishwajeet Kumar, Rudra Murthy, Jaydeep Sen, Ashish Mittal, Ganesh Ramakrishnan
| Challenge: | Large Language Models perform well on unseen tasks in English, but their abilities in non-English languages are less explored due to limited benchmarks and training data. |
| Approach: | They propose to release a large dataset for context-grounded question answering in 11 major Indian languages. |
| Outcome: | The Indic-QA Benchmark compared large datasets of large LLMs on extractive and abstractive tasks in 11 major Indian languages. |
PolyAudio: Advancing Multi-Audio Reasoning in Large Audio Language Models with Interleaved Multi-Audio Contexts (2026.findings-acl)
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Sonal Kumar, Sreyan Ghosh, Yueqian Lin, S Sakshi, Ashish Seth, Yiran Chen, Ramani Duraiswami, Dinesh Manocha
| Challenge: | Large Audio Language Models have shown impressive performance on single-clip tasks . however, their ability to reason over interleaved multi-audio contexts remains limited . |
| Approach: | They propose a LALM that targets multi-audio understanding via instruction tuning rather than massive-scale pre-training. |
| Outcome: | The proposed model outperforms baseline models on multi-audio tasks while maintaining robustness. |