AIConfPaper
← Search

Khushbu Pahwa

10 accepted papers

2026

Linguistic Properties and Model Scale in Brain Encoding: From Small to Compressed Language Models

Subba Reddy Oota, Satya Sai Srinath Namburi GNVV, Vijay Rowtula, Khushbu Pahwa, Anant Khandelwal, Manish Gupta +2
ICML 2026spotlight

Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains or which representational properties are responsible. Although larger models often yield better task performance and brain alignment, the…

Cited by 0Source ↗Scholar ↗
2025

Aligning Text/Speech Representations from Multimodal Models with MEG Brain Activity During Listening

Padakanti Srijith, Khushbu Pahwa, Radhika Mamidi, Bapi Raju Surampudi, Manish Gupta, Subba Reddy Oota
EMNLP 2025

Although speech language models are expected to align well with brain language processing during speech comprehension, recent studies have found that they fail to capture brain-relevant semantics beyond low-level features. Surprisingly, text-based language models exhibit stronger alignment with brai

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection

Michelle Yuan, Khushbu Pahwa, Shuaichen Chang, Mustafa Devrim Kaba, MONICA SUNKARA, Jiarong Jiang +2
NeurIPS 2025poster

Designing effective agentic systems requires the seamless composition and integration of agents, tools, and models within dynamic and uncertain environments. Most existing methods rely on static, semantic retrieval approaches for tool or agent discovery. However, effective reuse and composition of e…

Cited by 0Source ↗Scholar ↗
2025

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)

SUBBA REDDY OOTA, Akshett Rai Jindal, Ishani Mondal, Khushbu Pahwa, Satya Sai Srinath Namburi GNVV, Manish Shrivastava +3
ICLR 2025poster

Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models—through increased size, instruction-tuning, and multimodality—has led to better representational alignment with neural data…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts

Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa +3
ACL 2025long

Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a significant challenge. We introduce **EpMAN** – a method for processing long contexts in an episodic memory module while holis…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows

Kirolos Ataallah, Eslam Mohamed Bakr, Mahmoud Ahmed, Chenhui Gou, Khushbu Pahwa, Jian Ding +1
EMNLP 2025

Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a significant challenge for multi-modal models. Existing benchmarks often fail to test the full range of cognitive skills needed to process these temporally rich and narratively complex

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Multi-modal brain encoding models for multi-modal stimuli

SUBBA REDDY OOTA, Khushbu Pahwa, mounika marreddy, Maneesh Kumar Singh, Manish Gupta, Bapi Raju Surampudi
ICLR 2025poster

Despite participants engaging in unimodal stimuli, such as watching images or silent videos, recent work has demonstrated that multi-modal Transformer models can predict visual brain activity impressively well, even with incongruent modality representations. This raises the question of how accuratel…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking

Mert Kosan, Samidha Verma, Burouj Armgaan, Khushbu Pahwa, Ambuj Singh, Sourav Medya +1
ICLR 2024poster

Numerous explainability methods have been proposed to shed light on the inner workings of GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the interrogative aspects of these evaluations lack diversity. As a result, various facets of explainability pertaining to GN…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗
2023

FACTIFY3M: A benchmark for multimodal fact verification with explainability through 5W Question-Answering

Megha Chakraborty, Khushbu Pahwa, Anku Rani, Shreyas Chatterjee, Dwip Dalal, Harshit Dave +12
EMNLP 2023long main

Combating disinformation is one of the burning societal crises - about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opin…

Cited by 0Source ↗Scholar ↗