Papers by Manish Gupta
Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation (2025.naacl-industry)
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| Challenge: | GraphQL is a flexible alternative to REST APIs, but generating complex queries remains challenging. |
| Approach: | They propose a framework that integrates GraphQL schemas with natural language inputs to improve query generation accuracy. |
| Outcome: | The proposed framework improves performance on a publicly available complex GraphQL dataset. |
Frugal Prompting for Dialog Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are used in natural language processing tasks with an unrealistic speed and effectiveness. |
| Approach: | They propose more compact ways of providing dialog history information while ensuring good performance and reducing model’s inference-API costs. |
| Outcome: | The proposed models have the optimal usable-information density while maintaining good performance and reducing model’s inference-API costs. |
TripTide: A Benchmark for Adaptive Travel Planning under Disruptions (2026.findings-acl)
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Priyanshu Karmakar, Soumyabrata Chaudhuri, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh
| Challenge: | Recent work has shown the promise of Large Language Models (LLMs) for personalized, constraint-aware travel itinerary generation, but real-world travel often involves disruptions such as transit cancellations, weather-related closures, or overbooked attractions. |
| Approach: | They propose a benchmark to evaluate the ability of Large Language Models (LLMs) to revise travel itineraries under realistic disruptions. |
| Outcome: | The proposed benchmark evaluates the ability of Large Language Models (LLMs) to revise travel itineraries under real-world disruption scenarios. |
How does the brain process syntactic structure while listening? (2023.findings-acl)
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| Challenge: | Syntactic parsing is the task of assigning a syntactical structure to a sentence. |
| Approach: | They investigate the predictive power of the brain encoding models in three settings: individual performance of constituency and dependency parsing based embedding methods, relative effectiveness of each of the syntactic parsers, and relative importance of syntaktic information versus semantic information using BERT embeddngs. |
| Outcome: | The proposed methods explain variance in brain regions, especially in listening tasks, and the relative predictive power of the various syntax parsers is unexplored. |
Chat-Ghosting: Methods for Auto-Completion in Dialog Systems (2026.eacl-long)
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| Challenge: | Ghosting is a type-ahead completion task that predicts a user's intended input for inline query auto-completion (QAC). |
| Approach: | They propose to use ghosting to predict a user's intended input for inline query auto-completion by suggesting completions to incomplete queries. |
| Outcome: | The proposed method outperforms deep learning and deep learning methods with and without dialog context for ghosting. |
Aligning Text/Speech Representations from Multimodal Models with MEG Brain Activity During Listening (2025.emnlp-main)
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Padakanti Srijith, Khushbu Pahwa, Radhika Mamidi, Bapi Raju Surampudi, Manish Gupta, Subba Reddy Oota
| Challenge: | Recent studies have found that speech language models fail to capture brain-relevant semantics beyond low-level features. |
| Approach: | They analyze multimodal models to assess their alignment with MEG brain recordings . they find text embeddings from multimodal and unimodal models significantly outperform unilateral models . |
| Outcome: | a new study shows that text-based models outperform unimodal models in alignment with brain recordings during naturalistic story listening. |
Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation (P18-1)
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| Challenge: | Relation Schema Induction (RSI) is a problem of identifying type signatures of arguments from unlabeled text. |
| Approach: | They propose a framework for inducing higher-order relation schemata from unlabeled text. |
| Outcome: | The proposed framework helps in dealing with sparsity and induces higher-order relation schemata. |
Knowledge-Aware Reasoning over Multimodal Semi-structured Tables (2024.findings-emnlp)
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Suyash Mathur, Jainit Bafna, Kunal Kartik, Harshita Khandelwal, Manish Shrivastava, Vivek Gupta, Mohit Bansal, Dan Roth
| Challenge: | Existing datasets for tabular question answering focus on text within cells, but real-world data is multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content. |
| Approach: | They propose a dataset to assess whether current AI models can perform knowledge-aware reasoning on multimodal structured data. |
| Outcome: | The proposed dataset is a robust benchmark for advancing AI’s comprehension and capabilities in analyzing multimodal structured data. |
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts (2020.coling-main)
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Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, Ponnurangam Kumaraguru
| Challenge: | Existing studies on estimating the severity of abuse and the target of online abuse have focused on detecting and curtailment of such types of abuse. |
| Approach: | They propose to analyze online abuse from the perspective of presence, severity and target of abusive behavior from 7,601 posts from Gab and to estimate the severity of abuse. |
| Outcome: | The proposed system achieves 80% accuracy for abuse presence, 82% accuracy for abusive target prediction, and 65% accuracy for severity prediction. |
Leveraging Data Recasting to Enhance Tabular Reasoning (2022.findings-emnlp)
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| Challenge: | Existing approaches to create tabular inference data are limited by human annotation and synthetic generation. |
| Approach: | They propose a framework for semi-automatically recasting tabular data to make use of both approaches. |
| Outcome: | The proposed framework can be used to build tabular NLI instances from five datasets. |
ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos (2024.emnlp-main)
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| Challenge: | Existing studies on question generation from videos are mostly focused on generating questions about common objects and attributes. |
| Approach: | They propose a model architecture combining Transformers, rich context signals and a combination of cross-entropy and contrastive loss function to encourage entity-centric question generation. |
| Outcome: | The proposed system yields BLEU, ROUGE, CIDEr, and METEOR scores of 71.3, 78.6, 7.31, and 81.9. |
Representation Learning for Conversational Data using Discourse Mutual Information Maximization (2022.naacl-main)
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Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal
| Challenge: | Existing language modeling pretraining objectives do not take structural information of conversational text into account. |
| Approach: | They propose a structure-aware Mutual Information based loss-function DMI for training dialog-representation models that captures the inherent uncertainty in response prediction. |
| Outcome: | The proposed model outperforms strong baseline models on nine diverse tasks. |
Multi-label Categorization of Accounts of Sexism using a Neural Framework (D19-1)
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Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, Vasudeva Varma
| Challenge: | Sexism manifests in blatant as well as subtle ways, authors say . existing work on sexism classification has limitations in terms of categories used . authors: categorization of accounts of sexist behavior can aid in countering sextism . |
| Approach: | They propose a neural solution that can combine sentence representations with distributional and linguistic word embeddings. |
| Outcome: | a new method outperforms deep learning and traditional methods by an appreciable margin . the proposed method outpersforms several deep learning as well as traditional baselines by an approval margin compared to baselines . |
CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing contrastive decoding methods that handle conflict lack adaptability and can degrade performance in low conflict settings. |
| Approach: | They propose a token-level algorithm for principled conflict resolution and enhanced faithfulness that resolves conflict by utilizing confidence-aware measures and the generalized divergence between parametric and contextual distributions. |
| Outcome: | The proposed algorithm achieves 9.2 points on average in QA, summarization, and long-form question answering (LFQA) benchmarks and improves factuality by 2.5 points on the key benchmarks. |
Multimodal Persona Based Generation of Comic Dialogs (2023.acl-long)
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| Challenge: | Existing models for persona based dialogue generation for comic strips encode two-party dialogues and do not account for visual information. |
| Approach: | They propose a multimodal persona-based architecture to generate dialogues for the next panel in comic strips. |
| Outcome: | The proposed paradigm reduces the perplexity score by 10 points over existing models . the novel dataset, ComSet, contains 54K comic strips . |
Learning to Ask: Multi-Decoder Fine-Tuning for Multi-Hop Visual Question Generation with External Knowledge (2026.findings-eacl)
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| Challenge: | Traditional supervised QG methods rely on tokenlevel alignment with fixed gold labels struggle to capture diverse valid question formulations. |
| Approach: | They propose a model-agnostic framework that integrates multimodal inputs with a multi-decoder architecture to optimize for multiple labels per sample. |
| Outcome: | The proposed framework improves fluency, reasoning depth, and relevance of visual questions. |
Visio-Linguistic Brain Encoding (2022.coling-1)
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| Challenge: | Existing studies have failed to explore co-attentive multi-modal modeling for visual and text reasoning. |
| Approach: | They propose to use image and multi-modal Transformers to reconstruct fMRI brain activity . they use two popular datasets to study visual and text reasoning . |
| Outcome: | The proposed model outperforms existing models on two popular datasets . the results raise the question whether visual processing is affected implicitly by linguistic processing . |
Bilingual Tabular Inference: A Case Study on Indic Languages (2022.naacl-main)
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| Challenge: | Existing studies on Tabular Natural Language Inference (TNLI) focus on monolingual settings where tabular premise and hypothesis are in the same language. |
| Approach: | They propose a task where tabular premise and hypothesis are in two languages . they translate textual hypotheses from an English-indic TNLI dataset into eleven major languages - english and indic . |
| Outcome: | The proposed model performs well on a bilingual dataset in English and in 11 major Indian languages. |
TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning (2025.acl-long)
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Soumyabrata Chaudhuri, Pranav Purkar, Ritwik Raghav, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh
| Challenge: | Existing benchmarks such as TravelPlanner and TravelPlann+ rely on semi-synthetic data and lack key real-world constraints. |
| Approach: | They propose a spatio-temporally coherent travel planning dataset incorporating real-world constraints, including public transit schedules, public events, varied attraction categories, and user personas for enhanced personalization. |
| Outcome: | The proposed dataset significantly improves meal scheduling, improving performance from 61% to 80% in the 7-day scenario. |
TechING: Towards Real World Technical Image Understanding via VLMs (2026.findings-eacl)
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| Challenge: | Modern day vision language models struggle when it comes to understanding technical diagrams . a large synthetically generated corpus is needed to train and evaluate VLMs on hand-drawn images . |
| Approach: | They propose a large synthetically generated corpus for training VLMs and evaluate them on hand-drawn images. |
| Outcome: | The proposed model improves ROUGE-L performance of Llama 3.2 11B-instruct by 2.14x on synthetic images on real-world images. |
SCULPT: Systematic Tuning of Long Prompts (2025.acl-long)
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Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra, Parag Agrawal, Manish Gupta
| Challenge: | Existing methods for prompt optimization struggle with longer, more complex ones, often risking information loss and being sensitive to small perturbations. |
| Approach: | They propose a framework that treats prompt optimization as a hierarchical tree refinement problem and uses a Critic-Actor framework to generate reflections and apply actions to refine the prompt. |
| Outcome: | The proposed framework produces more stable and interpretable prompt modifications, ensuring better generalization across tasks. |
Predicting Clickbait Strength in Online Social Media (2020.coling-main)
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| Challenge: | Clickbaits are sensational, provocative or controversial posts that entice readers to click on them. |
| Approach: | They propose to model clickbait strength prediction using transformers to predict clickbaiting intensity. |
| Outcome: | The proposed model outperforms existing methods on a benchmark dataset with 39K posts on 3K posts. |
Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities (2025.findings-emnlp)
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| Challenge: | Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. |
| Approach: | They propose a framework that identifies and prompts users to enrich their posts by eliciting missing support attributes. |
| Outcome: | The proposed framework improves engagement and elicits missing information from posts. |
GraphQL Query Generation: A Large Training and Benchmarking Dataset (2024.emnlp-industry)
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Manish Kesarwani, Sambit Ghosh, Nitin Gupta, Shramona Chakraborty, Renuka Sindhgatta, Sameep Mehta, Carlos Eberhardt, Dan Debrunner
| Challenge: | GraphQL is a powerful query language for APIs, but crafting complex GraphqL queries can be challenging. |
| Approach: | a team of researchers has created a large-scale, cross-domain text-to-GraphQL query operation dataset . the dataset includes 10,940 training triples spanning 185 cross-source data stores and 957 test triples over 14 data stores. |
| Outcome: | The proposed dataset includes 10,940 training triples and 957 test triples over 14 data stores. |
Multi-view and Cross-view Brain Decoding (2022.coling-1)
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| Challenge: | a recent study has shown that brain decoding models can decode concepts from single view . a multi-view decoder can take brain recordings for any view as input and predict the concept . |
| Approach: | They propose to build a multi-view decoder that can take brain recordings for any view as input and predict the concept. |
| Outcome: | The proposed systems can decode concepts from brain recordings from any view . the proposed systems have 0.68 pairwise accuracy across view pairs and 0.8 average pairwise precision across tasks. |
VisToT: Vision-Augmented Table-to-Text Generation (2022.emnlp-main)
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| Challenge: | Existing models for data-to-text generation are wrongly generating estate in the output text. |
| Approach: | They propose a task that incorporates visual cues from tables and associated images to generate relevant text. |
| Outcome: | The proposed task incorporates visual cues from tables and associated images to generate relevant text. |
MAPWise: Evaluating Vision-Language Models for Advanced Map Queries (2025.naacl-long)
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| Challenge: | Vision-language models excel at tasks requiring joint understanding of visual information and natural language. |
| Approach: | They propose to use choropleth maps to answer questions from three geographical regions in the United States, India, China as question templates. |
| Outcome: | The proposed model outperforms other models in the area of visual language and visual question answering. |
On Robustness of Finetuned Transformer-based NLP Models (2023.findings-emnlp)
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| Challenge: | Pretrained Transformer-based language models have been finetuned for a large number of tasks. |
| Approach: | They characterize changes between pretrained and finetuned models with CKA and STIR metrics. |
| Outcome: | The proposed models are more robust to perturbations than BERT and T5 on classification tasks and generation tasks. |
Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning (2022.tacl-1)
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| Challenge: | Existing models that claim to reason about evidence should avoid spurious patterns . tabular inputs are well-suited for the study—they admit systematic probes . |
| Approach: | They propose to use tabular data to test whether models can reason about evidence . they show that a RoBERTa-based model fails to reason on the following counts . |
| Outcome: | The proposed model fails to reason on tabular data on the following counts . the model is over-sensitive to annotation artifacts and ignores relevant parts of the evidence . |
Router-Suggest: Dynamic Routing for Multimodal Auto-Completion in Visually-Grounded Dialogs (2026.eacl-industry)
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| Challenge: | a task that grounds predictions in multimodal context is essential for chatbots, chatbot systems and healthcare consultations. |
| Approach: | They propose a task that grounds predictions in multimodal context to better capture user intent. |
| Outcome: | The proposed task can be used to predict upcoming characters in live chats using partially typed text and visual cues. |
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient approach for fine-tuning large language models. |
| Approach: | They propose a low-rank Adaptation framework that automatically selects and merges LoRA adapters at the instance level without additional training. |
| Outcome: | The proposed framework outperforms training-based baselines on some tasks upto a margin of 3.6% while remaining competitive on other tasks and maintaining inference throughput. |
LRPLAN: A Multi-Agent Collaboration of Large Language and Reasoning Models for Planning with Implicit & Explicit Constraints (2025.findings-emnlp)
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| Challenge: | LRPlan is a language-based multi-agent system for complex planning problems . large language models are often unable to maintain consistency across the planning process . |
| Approach: | They propose a language-based multi-agent architecture where LLM and LRM agents collaborate at training time to abstract important patterns, heuristics and insights about the domain. |
| Outcome: | The proposed language-based multi-agent architecture outperforms existing models and makes it publicly available. |
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? (2022.naacl-main)
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| Challenge: | Existing literature has focused on pretrainer-based text-driven brain encoding models . however, few studies have explored the efficacy of task-specific learning of Transformers . |
| Approach: | They propose to use ten popular natural language processing tasks to learn Transformer representations for predicting brain responses. |
| Outcome: | The proposed model predicts brain activity across the whole brain. |
TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation (2025.findings-acl)
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| Challenge: | a rubric-based evaluation framework for tables often overlooks subtle structural and content-level discrepancies. |
| Approach: | They propose a rubric-based evaluation framework that integrates structural descriptors with contextual signals to enable more precise and consistent table comparison. |
| Outcome: | The proposed framework is based on a multi-domain benchmark with human annotations and perturbations. |
FIND: Toward Multimodal Financial Reasoning and Question Answering for Indic Languages (2026.findings-acl)
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| Challenge: | Existing benchmarks for numerical reasoning in multilingual Indic languages are inadequate . e.g., FinVQA is a framework for evaluating financial numerical reasoning . |
| Approach: | They propose a framework that combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning. |
| Outcome: | The proposed framework spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil . it combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning . |
A Workbench for Rapid Generation of Cross-Lingual Summaries (L18-1)
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| Challenge: | a tool for automating cross-lingual information access is needed in multilingual societies . current state of machine translation is not able to generate publishable articles from English . |
| Approach: | They propose a web-based tool for human editing of cross-lingual summaries . it generates publishable summary in a number of Indian Languages for news articles originally published in english . |
| Outcome: | The proposed tool can generate publishable summaries in multiple languages with minimal human effort and collect detailed logs on the process. |
USDC: A Dataset of ̲User ̲Stance and ̲Dogmatism in Long ̲Conversations (2025.findings-acl)
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| Challenge: | Previously, studies on stance and dogmatism in user conversations have focused on training models using annotated datasets at the post level, treating each post as independent and randomly sampling posts from conversation threads. |
| Approach: | They build a dataset for studying user opinion fluctuations in 764 long multi-user Reddit conversation threads, called USDC. |
| Outcome: | The proposed dataset analyzes user opinion fluctuations in 764 long multi-user Reddit conversation threads. |