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Manish Gupta

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11 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

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

Anuja Negi
SUBBAREDDY OOTA
Anwar Nunez-Elizalde
Manish Gupta
Fatma Deniz

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 studies provide strong evidence for a shared semantic system in bilingual individuals. Here, we investigate whether fine-tuning language models with bilingual brain data changes model representations in a way that improves them across multiple languages. To test this, we fine-tune monolingual and multilingual language models using brain activity recorded while bilingual participants read stories in English and Chinese. We then evaluate how well these representations generalize to the bilingual participants’ first language, their second language, and several other languages that the participants are not fluent in. We assess the fine-tuned language models on brain encoding performance and downstream NLP tasks. Our results show that bilingual brain-informed fine-tuned language models outperform their vanilla (pretrained) counterparts in both brain encoding performance and most downstream NLP tasks across multiple languages. These findings suggest that brain-informed fine-tuning improves multilingual understanding in language models, offering a bridge between cognitive neuroscience and NLP research. We make our code publicly available.

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TMLR Journal 2025 Journal Article

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

SUBBA REDDY OOTA
Zijiao Chen
Manish Gupta
Bapi Raju Surampudi
Gael Jobard
Frederic Alexandre
Xavier Hinaut

Can artificial intelligence unlock the secrets of the human brain? How do the inner mechanisms of deep learning models relate to our neural circuits? Is it possible to enhance AI by tapping into the power of brain recordings? These captivating questions lie at the heart of an emerging field at the intersection of neuroscience and artificial intelligence. Our survey dives into this exciting domain, focusing on human brain recording studies and cutting-edge cognitive neuroscience datasets that capture brain activity during natural language processing, visual perception, and auditory experiences. We explore two fundamental approaches: encoding models, which attempt to generate brain activity patterns from sensory inputs; and decoding models, which aim to reconstruct our thoughts and perceptions from neural signals. These techniques not only promise breakthroughs in neurological diagnostics and brain-computer interfaces but also offer a window into the very nature of cognition. In this survey, we first discuss popular representations of language, vision, and speech stimuli, and present a summary of neuroscience datasets. We then review how the recent advances in deep learning transformed this field, by investigating the popular deep learning based encoding and decoding architectures, noting their benefits and limitations across different sensory modalities. From text to images, speech to videos, we investigate how these models capture the brain's response to our complex, multimodal world. While our primary focus is on human studies, we also highlight the crucial role of animal models in advancing our understanding of neural mechanisms. Throughout, we mention the ethical implications of these powerful technologies, addressing concerns about privacy and cognitive liberty. We conclude with a summary and discussion of future trends in this rapidly evolving field. Given the large amount of recently published work in the computational cognitive neuroscience (CCN) community, we believe that this survey provides an invaluable entry point for deep neural network (DNN) researchers looking to diversify into CCN research, inviting them to join in unraveling the ultimate puzzle: the human brain.

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ICML Conference 2025 Conference Paper

MOGIC: Metadata-infused Oracle Guidance for Improved Extreme Classification

Suchith Chidananda Prabhu
Bhavyajeet Singh
Anshul Mittal
Siddarth Asokan
Shikhar Mohan
Deepak Saini
Yashoteja Prabhu
Lakshya Kumar

Retrieval-augmented classification and generation models benefit from early-stage fusion of high-quality text-based metadata, often called memory, but face high latency and noise sensitivity. In extreme classification (XC), where low latency is crucial, existing methods use late-stage fusion for efficiency and robustness. To enhance accuracy while maintaining low latency, we propose MOGIC, a novel approach to metadata-infused oracle guidance for XC. We train an early-fusion oracle classifier with access to both query-side and label-side ground-truth metadata in textual form and subsequently use it to guide existing memory-based XC disciple models via regularization. The MOGIC algorithm improves precision@1 and propensity-scored precision@1 of XC disciple models by 1-2% on six standard datasets, at no additional inference-time cost. We show that MOGIC can be used in a plug-and-play manner to enhance memory-free XC models such as NGAME or DEXA. Lastly, we demonstrate the robustness of the MOGIC algorithm to missing and noisy metadata. The code is publicly available at https: //github. com/suchith720/mogic.

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AAAI Conference 2025 Conference Paper

PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures

Shreya Shukla
Nakul Sharma
Manish Gupta
Anand Mishra

Writing comprehensive and accurate descriptions of technical drawings in patent documents is crucial to effective knowledge sharing and enabling the replication and protection of intellectual property. However, automation of this task has been largely overlooked by the research community. To this end, we introduce PatentDesc-355K, a novel large-scale dataset containing ∼355K patent figures along with their brief and detailed textual descriptions extracted from more than 60K US patent documents. In addition, we propose PatentLMM – a novel large multimodal model specifically tailored to generate high-quality descriptions of patent figures. Our proposed PatentLMM comprises two key components: (i) PatentMME, a specialized multimodal vision encoder that captures the unique structural elements of patent figures, and (ii) PatentLLaMA, a domain-adapted version of LLaMA fine-tuned on a large collection of patents. Our extensive experiments demonstrate that training a vision encoder specifically designed for patent figures significantly boosts the performance, generating coherent descriptions compared to fine-tuning similar-sized off-the-shelf multimodal models. PatentDesc-355K and PatentLMM pave the way for automating the understanding of patent figures, enabling efficient knowledge sharing and faster drafting of patent documents.

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TMLR Journal 2025 Journal Article

ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models

Yaswanth Narsupalli
Abhranil Chandra
Sreevatsa Muppirala
Manish Gupta
Pawan Goyal

Assessing the quality of generative model outputs from large language models (LLMs) or vision-language models (VLMs), poses significant challenges. Traditional evaluation methods either rely on human assessment which is resource-intensive and not scalable or on automatic metrics that often correlate poorly with human preferences. Another approach is to train dedicated neural evaluators, but this typically requires substantial training data and compute. In this study, we thus introduce ReFeR, a tuning-free framework for evaluating generative outputs including both text and images, using a two-level hierarchy of pre-trained LLM and VLM evaluators. This multi-agent hierarchical strategy leverages additional compute at inference time by orchestrating multiple models and utilizing the increased test-time reasoning to boost performance. By having models themselves provide feedback and final judgments, ReFeR reduces the dependence on human evaluation. We rigorously evaluate ReFeR on four diverse evaluation benchmarks, where it surpasses prior methods in accuracy while also generating constructive feedback useful for downstream distillation and self-improvement via finetuning. Interestingly, ReFeR is also applicable for reasoning tasks - experiments on four reasoning benchmarks show ReFeR’s superior collective reasoning abilities. We present two variants of the framework: ReFeR-Turbo, optimized for accelerated performance, and ReFeR-Lite, offering a more test-time compute efficient solution. ReFeR-Lite is $\sim12-14\times$ more compute efficient than previous works while being comparably accurate to ReFeR-Turbo.

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AAAI Conference 2024 Conference Paper

Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex Interactions

Prajwal Gatti
Kshitij Parikh
Dhriti Prasanna Paul
Manish Gupta
Anand Mishra

Non-native speakers with limited vocabulary often struggle to name specific objects despite being able to visualize them, e.g., people outside Australia searching for ‘numbats.’ Further, users may want to search for such elusive objects with difficult-to-sketch interactions, e.g., “numbat digging in the ground.” In such common but complex situations, users desire a search interface that accepts composite multimodal queries comprising hand-drawn sketches of “difficult-to-name but easy-to-draw” objects and text describing “difficult-to-sketch but easy-to-verbalize” object's attributes or interaction with the scene. This novel problem statement distinctly differs from the previously well-researched TBIR (text-based image retrieval) and SBIR (sketch-based image retrieval) problems. To study this under-explored task, we curate a dataset, CSTBIR (Composite Sketch+Text Based Image Retrieval), consisting of ~2M queries and 108K natural scene images. Further, as a solution to this problem, we propose a pretrained multimodal transformer-based baseline, STNet (Sketch+Text Network), that uses a hand-drawn sketch to localize relevant objects in the natural scene image, and encodes the text and image to perform image retrieval. In addition to contrastive learning, we propose multiple training objectives that improve the performance of our model. Extensive experiments show that our proposed method outperforms several state-of-the-art retrieval methods for text-only, sketch-only, and composite query modalities. We make the dataset and code available at: https://vl2g.github.io/projects/cstbir.

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ICLR Conference 2024 Conference Paper

Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction

Anirudh Buvanesh
Rahul Chand
Jatin Prakash
Bhawna Paliwal
Mudit Dhawan
Neelabh Madan
Deepesh Hada
Vidit Jain

Extreme Classification (XC) architectures, which utilize a massive One-vs-All (OvA) classifier layer at the output, have demonstrated remarkable performance on problems with large label sets. Nonetheless, these architectures falter on tail labels with few representative samples. This phenomenon has been attributed to factors such as classifier over-fitting and missing label bias, and solutions involving regularization and loss re-calibration have been developed. This paper explores the impact of label variance - a previously unexamined factor - on the tail performance in extreme classifiers. It also develops a method to systematically reduce label variance in XC by transferring the knowledge from a specialized tail-robust teacher model to the OvA classifiers. For this purpose, it proposes a principled knowledge distillation framework, LEVER, which enhances the tail performance in extreme classifiers with formal guarantees on generalization. Comprehensive experiments are conducted on a diverse set of XC datasets, demonstrating that LEVER can enhance tail performance by around 5\% and 6\% points in PSP and coverage metrics, respectively, when integrated with leading extreme classifiers. Moreover, it establishes a new state-of-the-art when added to the top-performing Renee classifier. Extensive ablations and analyses substantiate the efficacy of our design choices. Another significant contribution is the release of two new XC datasets that are different from and more challenging than the available benchmark datasets, thereby encouraging more rigorous algorithmic evaluation in the future. Code for LEVER is available at: aka.ms/lever.

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ICML Conference 2024 Conference Paper

OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification

Shikhar Mohan
Deepak Saini
Anshul Mittal
Sayak Ray Chowdhury
Bhawna Paliwal
Jian Jiao 0007
Manish Gupta
Manik Varma

The objective in eXtreme Classification (XC) is to find relevant labels for a document from an exceptionally large label space. Most XC application scenarios have rich auxiliary data associated with the input documents, e. g. , frequently clicked webpages for search queries in sponsored search. Unfortunately, most of the existing XC methods do not use any auxiliary data. In this paper, we propose a novel framework, Online Auxiliary Knowledge (OAK), which harnesses auxiliary information linked to the document to improve XC accuracy. OAK stores information learnt from the auxiliary data in a knowledge bank and during a forward pass, retrieves relevant auxiliary knowledge embeddings for a given document. An enriched embedding is obtained by fusing these auxiliary knowledge embeddings with the document’s embedding, thereby enabling much more precise candidate label selection and final classification. OAK training involves three stages. (1) Training a linker module to link documents to relevant auxiliary data points. (2) Learning an embedding for documents enriched using linked auxiliary information. (3) Using the enriched document embeddings to learn the final classifiers. OAK outperforms current state-of-the-art XC methods by up to $\sim 5 %$ on academic datasets, and by $\sim 3 %$ on an auxiliary data-augmented variant of LF-ORCAS-800K dataset in Precision@1. OAK also demonstrates statistically significant improvements in sponsored search metrics when deployed on a large scale search engine.

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IJCAI Conference 2023 Conference Paper

Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering

Abhirama Subramanyam Penamakuri
Manish Gupta
Mithun Das Gupta
Anand Mishra

We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-based visual question answering (or RETVQA in short). The RETVQA is distinctively different and more challenging than the traditionally-studied Visual Question Answering (VQA), where a given question has to be answered with a single relevant image in context. Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. Further, we introduce the largest dataset in this space, namely RETVQA, which has the following salient features: multi-image and retrieval requirement for VQA, metadata-independent questions over a pool of heterogeneous images, expecting a mix of classification-oriented and open-ended generative answers. Our proposed framework achieves an accuracy of 76. 5% and a fluency of 79. 3% on the proposed dataset, namely RETVQA and also outperforms state-of-the-art methods by 4. 9% and 11. 8% on the image segment of the publicly available WebQA dataset on the accuracy and fluency metrics, respectively.

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NeurIPS Conference 2023 Conference Paper

Joint processing of linguistic properties in brains and language models

SUBBAREDDY OOTA
Manish Gupta
Mariya Toneva

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 brain versus language models. We investigate this correspondence via a direct approach, in which we eliminate information related to specific linguistic properties in the language model representations and observe how this intervention affects the alignment with fMRI brain recordings obtained while participants listened to a story. We investigate a range of linguistic properties (surface, syntactic, and semantic) and find that the elimination of each one results in a significant decrease in brain alignment. Specifically, we find that syntactic properties (i. e. Top Constituents and Tree Depth) have the largest effect on the trend of brain alignment across model layers. These findings provide clear evidence for the role of specific linguistic information in the alignment between brain and language models, and open new avenues for mapping the joint information processing in both systems. We make the code publicly available https: //github. com/subbareddy248/lingprop-brain-alignment.

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AAAI Conference 2018 Conference Paper

Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization

Abhishek Singh
Manish Gupta
Vasudeva Varma

Automated multi-document extractive text summarization is a widely studied research problem in the ﬁeld of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the documentdependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC- 2004) indicate that our model shows signiﬁcant performance gain of around 1. 5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.

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