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SUBBA REDDY OOTA

14 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…

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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

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2025

Brain-Informed Fine-Tuning for Improved Multilingual Understanding in Language Models

Anuja Negi, SUBBA REDDY OOTA, Anwar O Nunez-Elizalde, Manish Gupta, Fatma Deniz
NeurIPS 2025poster

Recent studies have demonstrated that fine-tuning language models with brain data can improve their semantic understanding, although these findings have so far been limited to English. Interestingly, similar to the shared multilingual embedding space of pretrained multilingual language models, human…

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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…

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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…

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2025

USDC: A Dataset of  ̲User  ̲Stance and  ̲Dogmatism in Long  ̲Conversations

Mounika Marreddy, Subba Reddy Oota, Venkata Charan Chinni, Manish Gupta, Lucie Flek
ACL 2025finding

Analyzing user opinion changes in long conversation threads is extremely critical for applications like enhanced personalization, market research, political campaigns, customer service, targeted advertising, and content moderation. Unfortunately, previous studies on stance and dogmatism in user conv…

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2024

Speech language models lack important brain-relevant semantics

Subba Reddy Oota, Emin Çelik, Fatma Deniz, Mariya Toneva
ACL 2024long

Despite known differences between reading and listening in the brain, recent work has shown that text-based language models predict both text-evoked and speech-evoked brain activity to an impressive degree. This poses the question of what types of information language models truly predict in the bra…

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2023

How does the brain process syntactic structure while listening?

Subba Reddy Oota, Mounika Marreddy, Manish Gupta, Raju Bapi
ACL 2023findings

Syntactic parsing is the task of assigning a syntactic structure to a sentence. There are two popular syntactic parsing methods: constituency and dependency parsing. Recent works have used syntactic embeddings based on constituency trees, incremental top-down parsing, and other word syntactic featur…

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2023

Joint processing of linguistic properties in brains and language models

SUBBA REDDY OOTA, Manish Gupta, Mariya Toneva
NeurIPS 2023poster

Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli. For a deeper understanding of this alignment, it is important to understand the correspondence between the detailed processing of linguistic information by the human…

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2023

On Robustness of Finetuned Transformer-based NLP Models

Pavan Kalyan Reddy Neerudu, SUBBA REDDY OOTA, mounika marreddy, venkateswara Rao Kagita, Manish Gupta
EMNLP 2023long findings

Transformer-based pretrained models like BERT, GPT-2 and T5 have been finetuned for a large number of natural language processing (NLP) tasks, and have been shown to be very effective. However, while finetuning, what changes across layers in these models with respect to pretrained checkpoints is und…

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2022

Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?

Subba Reddy Oota, Jashn Arora, Veeral Agarwal, Mounika Marreddy, Manish Gupta, Bapi Surampudi
NAACL 2022long

Several popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explored the efficacy of task-specific learned Transformer representations. In this work, we e…

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