MAPVERSE: A Benchmark for Geospatial Question Answering on Diverse Real-World Maps

Sharat Bhat, Harshita Khandelwal, Tushar Kataria, Vivek Gupta WACV 2026

Maps are powerful carriers of structured and contextual knowledge, encompassing geography, demographics, in- frastructure, and environmental patterns. Reasoning over such knowledge requires models to integrate spatial re- lationships, visual cues, real-world context, and domain- specific expertise-capabilities that current large language models (LLMs) and vision–language models (VLMs) still struggle to exhibit consistently. Yet, datasets used to bench- mark VLMs on map-based reasoning remain narrow in scope, restricted to specific domains, and heavily reliant on artificially generated content (outputs from LLMs or pipeline-based methods), offering limited depth for evaluat- ing genuine geospatial reasoning. To address this gap, we present MAPVERSE, a large-scale benchmark built on real- world maps. It comprises...

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The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs

Tejas Anvekar, Fenil Bardoliya, Pavan Turaga, Chitta Baral, Vivek Gupta WACV 2026

We present The Perceptual Observatory, a framework that characterizes MLLMs across verticals like: (i) simple vision tasks, such as face matching and text-in-vision comprehension capabilities; (ii) local-to-global understanding, encompassing image matching, grid pointing game, and attribute localization, which tests general visual grounding. Each vertical is instantiated with ground-truth datasets of faces and words,systematically perturbed through pixel-based augmentations and diffusion-based stylized illusions. The Perceptual Observatory moves beyond leaderboard accuracy to...

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Integrity Shield: A System for Ethical AI Use & Authorship Transparency in Assessments

Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi, Yash Shah, Tejas Anvekar, Vivek Gupta EACL 2026 (Demo)

Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF as- sessments, raising urgent concerns about aca- demic integrity and the reliability of grades and credentials. Existing watermarking tech- niques either operate at the token level or assume control over the model’s decoding process, making them ineffective when stu- dents query proprietary black-box systems with instructor-provided documents. We present IN- TEGRITY SHIELD, a document-layer wa- termarking system that embeds schema-aware, item-level watermarks into assessment PDFs while keeping their human-visible appearance unchanged. These watermarks consistently pre- vent MLLMs from answering shielded exam PDFs and encode stable, item-level signatures that can be reliably recovered from model or student responses. Across 30 exams...

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No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning

Abhishek Rajgaria,Kushagra Dixit, Mayank Vyas, Harshavardhan Kalalbandi, Dan Roth, Vivek Gupta AACL 2025

Temporal Table Reasoning poses a significant challenge for Large Language Models (LLMs), requiring effective reasoning to extract relevant insights. Despite existence of multiple prompting methods, their impact on table reasoning remains largely unexplored. Furthermore, model performance varies drastically across different table and context structures, making it difficult to determine an optimal approach. This work investigates multiple prompting technique on diverse table types to determine that performance depends on factors such as entity type, table structure, requirement of additional context and question complexity, with "NO" single method consistently outperforming others. To address this, we introduce SEAR, an adaptive prompting framework inspired by human reasoning that dynamically adjusts to context and integrates structured reasoning. SEAR_Unified, its cost-efficient variant. We also demonstrate that optional table refactoring (preprocessing) enhances both approaches when tables lack structural consistency. Our results demonstrate that SEAR prompts achieve superior performance across all table types compared to baseline prompting techniques...

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Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective

Tejas Anvekar, Krishna Singh Rajput, Chitta Baral, Vivek Gupta AACL 2025

Recent advances in multimodal question answering have primarily focused on combining heterogeneous modalities or fine-tuning multimodal large language models. While these approaches have shown strong performance, they often rely on a single, generalized reasoning strategy, overlooking the unique characteristics of each modality ultimately limiting both accuracy and interpretability. To address these limitations, we propose MAMMQA , a multi-agent QA framework for multimodal inputs spanning text, tables, and images. Our system includes...

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SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization

Sebastian Martinez, Naman Ahuja, Fenil Bardoliya, Chris Bryan, Vivek Gupta AACL 2025

We present a modular, interactive system SPORTSQL for natural language querying and visualization of dynamic sports data, with a focus on the English Premier League (EPL). The system translates user questions into executable SQL over a live, temporally indexed database constructed from real-time Fantasy Premier League (FPL) data. It supports both tabular and visual outputs, leveraging symbolic reasoning capabilities of Large Language Models (LLMs) for query parsing, schema linking, and visualization selection. To evaluate...

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InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

Anirudh Iyengar Kaniyar Narayana Iyengar, Srija Mukhopadhyay, Adnan Qidwai, Shubhankar Singh, Dan Roth, Vivek Gupta AACL 2025

We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task central to real-world applications such as scientific reporting, financial analysis, and public policy dashboards. Unlike prior benchmarks focusing on isolated, visually uniform charts, InterChart challenges models with diverse question types ranging from entity inference and trend correlation to numerical estimation and abstract multi-step reasoning grounded in 2-3 thematically or structurally related charts. We organize the benchmark into three tiers of increasing difficulty: (1) factual reasoning over individual charts, (2) integrative analysis across synthetically aligned chart sets, and (3) semantic inference over visually complex, real-world chart pairs. Our evaluation...

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Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents

Manan Suri, Puneet Mathur, Nedim Lipka, Franck Dernoncourt, Ryan A. Rossi, Vivek Gupta, Dinesh Manocha EMNLP 2025

Flowcharts are a critical tool for visualizing decision-making processes. However, their non-linear structure and complex visual-textual relationships make it challenging to interpret them using LLMs, as vision-language models frequently hallucinate nonexistent connections and decision paths when analyzing these diagrams. This leads to compromised reliability for automated flowchart processing in critical domains such as logistics, health, and engineering. We introduce the task of Fine-grained Flowchart Attribution, which traces specific components grounding a flowchart referring LLM response. Flowchart Attribution ensures the verifiability of LLM predictions and improves explainability by linking generated responses to the flowchart's structure. We propose FlowPathAgent, a neurosymbolic agent that performs fine-grained post hoc attribution through graph-based reasoning. It first segments...

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Weaver: Interweaving SQL and LLM for Table Reasoning

Rohit Khoja, Devanshu Gupta, Yanjie Fu, Dan Roth, Vivek Gupta EMNLP 2025

Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requiring semantic reasoning. While Large Language Models (LLMs) excel at understanding context, they face limitations with long input sequences. Existing approaches that combine SQL and LLMs typically rely on rigid, predefined work flows, limiting their adaptability to complex queries. To address these issues, we introduce Weaver , a modular pipeline that dynamically integrates SQL and LLMs for table-based question answering (TableQA). Weaver generates...

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TABARD: A Novel Benchmark for Tabular Anomaly Analysis, Reasoning and Detection

Manan Roy Choudhury, Anirudh Iyengar, Shikhhar Siingh, Sugeeth Puranam, Vivek Gupta EMNLP 2025

We study the capabilities of large language models (LLMs) in detecting fine-grained anomalies in tabular data. Specifically, we examine: (1) how well LLMs can identify diverse anomaly types—including factual, logical, temporal, and value-based errors; (2) the impact of prompt design and prompting strategies; and (3) the effect of table structure and anomaly type on detection accuracy. To this end, we introduce TABARD, a new benchmark constructed by perturbing tables from WikiTQ, FeTaQA, Spider, and BEAVER. The dataset spans multiple domains and eight anomaly categories, including paired clean and corrupted tables. We evaluate LLMs using direct, indirect, and Chain-of-Thought (CoT) prompting. Our results reveal notable limitations in standard prompting, especially for complex reasoning tasks and longer tables. To overcome these issues, we propose a unified framework combining multi-step prompting, self-verification, and constraint-based rule execution. Our approach...

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Federated Retrieval-Augmented Generation: A Systematic Mapping Study

Abhijit Chakraborty, Chahana Dahal, Vivek Gupta EMNLP 2025 (Findings)

Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL),which enables distributed model training without exposing raw data, with Retrieval-Augmented Generation (RAG), which improves the factual accuracy of language models by grounding outputs in external knowledge. As large language models are increasingly deployed in privacy-sensitive domains such as healthcare, finance, and personalized assistance, Federated RAG offers a promising framework for secure, knowledge-intensive natural language processing (NLP). To the best of our knowledge, this paper presents the first systematic mapping study of Federated RAG, covering literature published between 2020 and 2025. Following Kitchenham’s...

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M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals

MSVPJ Sathvik, Zuhair Hasan Shaik, Vivek Gupta EMNLP 2025 (Findings)

Mental health disorders are a global crisis. While various datasets exist for detecting such disorders, there remains a critical gap in identifying individuals actively seeking help. This paper introduces a novel dataset, M-HELP, specifically designed to detect help-seeking behavior on social media. The dataset goes beyond traditional labels by identifying not only help-seeking activity but also specific mental health disorders and their underlying causes, such as relationship challenges or financial stressors. AI models...

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MapIQ: Benchmarking Multimodal Large Language Models for Map Question Answering

V Srivastava, F Lei, S Mukhopadhyay, V Gupta, R Maciejewski COLM 2025

Recent advancements in multimodal large language models (MLLMs) have driven researchers to explore how well these models read data visualizations, e.g., bar charts, scatter plots. More recently, attention has shifted to visual question answering with maps (Map-VQA). However, Map-VQA research has primarily focused on choropleth maps, which cover only a limited range of thematic categories and visual analytical tasks. To address these gaps, we introduce MapIQ, a benchmark dataset comprising 14,706 question-answer pairs across three map types—choropleth maps, cartograms, and proportional symbol maps spanning topics from six distinct themes (e.g., housing, crime). We evaluate multiple MLLMs using six visual analytical tasks, comparing their performance against one another and a human baseline. An additional experiment...

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TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation

Vihang Pancholi, Jainit Sushil Bafna, Tejas Anvekar, Manish Shrivastava, Vivek Gupta ACL 2025

Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-level discrepancies. To address this, we propose a rubric-based evaluation frame work that integrates multi-level structural descriptors with fine-grained contextual signals, enabling more precise and consistent table comparison. Building on this, we introduce TabXEval, an eXhaustive and eXplainable two-phase evaluation framework. TabXEval first aligns reference and predicted...

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Map&Make: Schema Guided Text to Table Generation

Naman Ahuja, Fenil Bardoliya, Chitta Baral, Vivek Gupta ACL 2025

Transforming dense, unstructured text into interpretable tables—commonly referred to as Text-to-Table generation—is a key task in information extraction. Existing methods often overlook what complex information to extract and how to infer it from text. We present Map&Make, a versatile approach that decomposes text into atomic propositions to infer latent schemas, which are then used to generate tables capturing both qualitative nuances and quantitative facts. We evaluate...

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PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights

Adnan Qidwai, Srija Mukhopadhyay, Prerana Khatiwada, Dan Roth, Vivek Gupta ACL 2025(Demo)

Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows...

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LLM-Symbolic Integration for Robust Temporal Tabular Reasoning

Atharv Kulkarni, Kushagra Dixit, Vivek Srikumar, Dan Roth, Vivek Gupta ACL 2025

Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data—a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce TEMPTABQA-C, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating...

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GETReason: Enhancing Image Context Extraction through Hierarchical Multi-Agent Reasoning

Shikhhar Siingh, Abhinav Rawat, Chitta Baral, Vivek Gupta ACL 2025

Publicly significant images from events carry valuable contextual information with applications in domains such as journalism and education. However, existing methodologies of ten struggle to accurately extract this contextual relevance from images. To address this challenge, we introduce GETREASON(Geospatial Event Temporal Reasoning), a framework designed to go beyond surface level image descriptions and infer deeper con textual meaning. We hypothesize that extracting global event, temporal, and geospatial information from an image enables a more accurate understanding of its contextual significance. We also introduce a new metric GREAT (Geospatial, Reasoning and Event Accuracy with Temporal alignment) for a reasoning capturing evaluation. Our layered multi-agentic approach, evaluated...

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MAPWise: Evaluating Vision-Language Models for Advanced Map Queries

Srija Mukhopadhyay, Abhishek Rajgaria, Prerana Khatiwada, Manish Shrivastava, Dan Roth, Vivek Gupta NAACL 2025

Vision-language models (VLMs) excel at tasks requiring joint understanding of visual and linguistic information. A particularly promising yet under-explored application for these models lies in answering questions based on various kinds of maps. This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. Our benchmark...

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Leveraging LLM for Synchronizing Information Across Multilingual Tables

Siddharth Khincha, Tushar Kataria, Ankita Anand, Dan Roth, Vivek Gupta NAACL 2025

The vast amount of online information today poses challenges for non-English speakers, as much of it is concentrated in high-resource languages such as English and French. Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete. Recent research has sought to improve cross-language synchronization of Wikipedia tables using rule-based methods. These approaches can be effective, but they struggle with complexity and generalization. This paper explores large language models (LLMs) for multilingual information synchronization, using zero-shot prompting as a scalable solution. We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables, and evaluate LLM performance. Our findings...

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TRANSIENT TABLES: Evaluating LLMs’ Reasoning on Temporally Evolving Semi-structured Tables

Abhilash Shankarampeta, Harsh Mahajan, Tushar Kataria, Dan Roth, Vivek Gupta NAACL 2025

Humans continuously make new discoveries, and understanding temporal sequence of events leading to these breakthroughs is essential for advancing science and society. This ability to reason over time allows us to identify future steps and understand the effects of financial and political decisions on our lives. However, large language models (LLMs) are typically trained on static datasets, limiting their ability to perform effective temporal reasoning. To assess the temporal reasoning capabilities of LLMs, we present the TRANSIENTTABLES dataset, which comprises 3,971 questions derived from over 14,000 tables, spanning 1,238 entities across multiple time periods. We introduce a template-based question-generation pipeline that harnesses LLMs to refine both templates and questions. Additionally, we establish baseline...

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H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables

Nikhil Abhyankar, Vivek Gupta, Dan Roth, Chandan Reddy NAACL 2025

Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data analysis. Existing methods employ either textual reasoning, which excels in semantic interpretation but struggles with mathematical operations, or symbolic reasoning, which handles computations well but lacks semantic understanding. This paper introduces a novel algorithm H-STAR that integrates both symbolic and semantic (textual) approaches in a two-stage process to address these limitations. H-STAR employs: (1) step-wise table extraction using ‘multi-view’ column retrieval followed by row extraction, and (2) adaptive reasoning that adapts reasoning strategies based on question types, utilizing semantic reasoning for direct lookup and complex lexical queries while augmenting textual reasoning with symbolic reasoning support for quantitative and logical tasks. Our extensive...

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Enhancing Temporal Understanding in LLMs for Semi-structured Tables

Irwin Deng, Kushagra Dixit, Dan Roth, Vivek Gupta NAACL 2025 (Findings)

Temporal reasoning over tabular data presents substantial challenges for large language models (LLMs), as evidenced by recent research. In this study, we conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of LLMs. Our investigation leads to enhancements in TempTabQA, a benchmark specifically designed for tabular temporal question answering. We provide critical insights for enhancing LLM performance in temporal reasoning tasks with tabular data. Furthermore, we introduce a novel approach, C.L.E.A.R to strengthen LLM capabilities in this domain. Our findings demonstrate that our method im proves evidence-based reasoning across various models. Additionally, our experimental...

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NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models

Pranshu Pandya, Vatsal Gupta, Agney S Talwarr, Tushar Kataria, Dan Roth, Vivek Gupta NAACL 2025

Cognitive textual and visual reasoning tasks, including puzzles, series, and analogies, demand the ability to quickly reason, decipher, and evaluate patterns both textually and spatially. Due to extensive training on vast amounts of human-curated data, large language models (LLMs) and vision language models (VLMs) excel in common-sense reasoning tasks, but still struggle with more complex reasoning that demands deeper cognitive understanding. We introduce NTSEBENCH, a new dataset designed to evaluate cognitive multimodal reasoning and problem-solving skills of large models. The dataset contains 2,728 multiple-choice questions, accompanied by a total of 4,642 images, spanning 26 categories....

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Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets

Vatsal Gupta, Pranshu Pandya, Tushar Kataria, Vivek Gupta, Dan Roth
EMNLP 2024

Language models, characterized by their black box nature, often hallucinate and display sensitivity to input perturbations, causing concerns about trust. To enhance trust, it is imperative to gain a comprehensive understanding of the model’s failure modes and develop effective strategies to improve their performance. In this study, we introduce a methodology designed to examine how input perturbations affect language models across various scales, including pre-trained models and large language models (LLMs). Utilizing fine-tuning, we enhance the model’s robustness to input perturbations. Additionally, ...

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Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness

Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, Pritika Ramu, Vivek Gupta, Dan Roth
EMNLP 2024 (Findings)

Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field remain under-explored. This paper evaluates state-of-the-art VLMs on comprehensive datasets, developed specifically for this study, encompassing diverse question categories and chart formats. We investigate two key aspects: 1) the models' ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. Our analysis reveals significant performance variations based on question and chart types, highlighting both strengths and weaknesses of current models. Additionally,...

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Knowledge-Aware Reasoning over Multimodal Semi-structured Tables

Suyash Vardhan Mathur, Jainit Sushil Bafna, Kunal Kartik, Harshita Khandelwal, Manish Shrivastava, Vivek Gupta, Mohit Bansal, Dan Roth EMNLP 2024 (Findings)

Existing datasets for tabular question answering typically focus exclusively on text within cells. However, real-world data is inherently multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content in tables. With the evolution of AI models capable of multimodal reasoning, it is pertinent to assess their efficacy in handling such structured data. This study investigates whether current AI models can perform knowledge-aware reasoning on multimodal structured data. We explore their ability to reason on tables that integrate both images and text, introducing MMTABQA, a new dataset designed for this purpose. Our experiments...

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FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts

Shubhankar Singh, Purvi Chaurasia, Yerram Varun, Pranshu Pandya, Vatsal Gupta, Vivek Gupta, Dan Roth ACL 2024 (Findings)

Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. FlowVQA comprises 2,272 carefully generated and human-verified flowchart images from three distinct content sources, along with 22,413 diverse question-answer pairs, to test a spectrum of reasoning tasks, including information localization, decision-making, and logical progression. We conduct a thorough baseline evaluation on a suite of both open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. The results...

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Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering

Pragya Srivastava, Manuj Malik, Vivek Gupta, Tanuja Ganu, Dan Roth ACL 2024 (Findings)

Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with a hybrid of structured tables and unstructured text remain uncertain. This study explores LLMs’ mathematical reasoning on four financial tabular question-answering datasets: TATQA, FinQA, ConvFinQA, and Multihiertt. Through extensive experiments with various models and prompting techniques, we assess how LLMs adapt to complex tables and mathematical tasks. We focus on sensitivity to table complexity and performance variations with an increasing number of arithmetic reasoning steps. The results...

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ChartCheck: An Evidence-Based Fact-Checking Dataset over Real-World Chart Images

Mubashara Akhtar, Nikesh Subedi, Vivek Gupta, Sahar Tahmasebi, Oana Cocarascu, Elena Simperl ACL 2024 (Findings)

Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and communicate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against Chart: Evidence real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically...

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Enhancing Question Answering on Charts Through Effective Pre-training Tasks

Ashim Gupta, Vivek Gupta, Shuo Zhang, Yujie He, Ning Zhang, Shalin Shah BlackboxNLP 2024 (Findings)

To completely understand a document, the use of textual information is not enough. Under standing visual cues, such as layouts and charts, is also required. While the current state-of the-art approaches for document understanding (both OCR-based and OCR-free) work well, we have not found any other works conducting a thorough analysis of their capabilities and limitations. Therefore, in this work, we address the limitation of current VisualQA models when applied to charts and plots. To investigate shortcomings of the state-of-the-art models, we conduct a comprehensive behavioral analysis, using ChartQA as a case study. Our findings ...

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PAST PUBLICATIONS

TempTabQA: Temporal Question Answering for Semi-Structured Tables
Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar
Published at EMNLP 2023, Paper, Project Page

Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, Arpit Patil, Oana Cocarascu, Elena Simperl
Published at EMNLP 2023(Findings), Paper

InfoSync: Information Synchronization across Multilingual Semi-structured Tables
Sidharth Khincha, Chelsi Jain, Vivek Gupta, Tushar Kataria, Shuo Zhang
Published at ACL 2023, presented at Matching@ACL 2023 Project Page, Paper, Video, Poster, PPT

Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning,
Vivek Gupta, Shuo Zhang, Alakananda Vempala, Yujie He, Temma Choji, Vivek Srikumar
published at ACL 2022 Paper Poster [PPT] Video Media LinkedIn

Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning,
Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar
published at TACL 2022, presented at ACL 2022 [Paper][Preprint] [Poster] [PPT] [Video]

Bilingual Tabular Inference: A Case Study on Indic Languages
Chaitanya Agarwal*, Vivek Gupta*, Anoop Kunchukuttan, Manish Shrivastava
published at NAACL 2022 [Paper] [Preprint] [PPT] [Poster] [Video]

Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM model for Tabular Reasoning,
Yerram Varun*, Aayush Sharma*, Vivek Gupta*
to appear at DeeLIO-2022 @ACL 2022 [Paper] [Preprint] [Poster] [PPT] [Video]
Won Best Paper award at DeeLIO-2022

XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference,
Bhavnick Minhas*, Anant Shankhdhar*, Vivek Gupta*, Divyanshu Aggarwal, Shuo Zhang,
published at MML-2022 (non-archival) and FEVER-2022 (archival) @ACL 2022 [Preprint] [Poster] [PPT] [Video] [Media] [LinkedIn]

Enhancing Tabular Reasoning with Pattern Exploiting Training,
Abhilash Shankarampeta*, Vivek Gupta*, Shuo Zhang
to appear at SUKI-2022 (non-archival) [Preprint] [PPT] [Poster] [Video]
(Extended Version at AACL 2022) [Paper] [Project Page] [Media]

Efficient Realistic Data Generation Framework for Semi-Structured Tabular Inference,
Dibyakanti Kumar*, Vivek Gupta*, Soumya Sharma, Shuo Zhang
to appear at SUKI-2022(non-archival) [Preprint] [PPT] [Video] [Poster]
(Extended Version at EMNLP 2022) [Project Page] [Paper] [Media]

Leveraging Data Recasting to Enhance Tabular Reasoning,
Aashna Jena*, Vivek Gupta*, Manish Shrivastava, Julian Martin Eisenschlos
to appear at SUKI-2022 (non-archival) [Preprint] [Poster] [PPT] [Video]
(Extended Version at EMNLP 2022) [Project Page] [Paper] [Media] [Poster]

RetroNLU: Retrieval Augmented Task Oriented Semantic Parsing,
Vivek Gupta, Akshat Shrivastava, Adithya Sagar, Armen Aghajanyan, Denis Savenkov,
to appear at Spa-NLP-2022 (non-archival) and NLP4ConvAI-2022 (archival) @ACL 2022 [Paper] [Preprint] [Poster] [PPT] [Video]
Won Outstanding Paper award at NLP4ConvAI-2022

TabPert: An Effective Platform for Tabular Perturbation,
Nupur Jain,Vivek Gupta, Anshul Rai, Gaurav Kumar
Published at EMNLP 2021, Demo track [Paper] [Project Page][Preprint] [PPT] [Video] [Code]

Incorporating External Knowledge to Enhance Tabular Reasoning,
J. Neeraja*, Vivek Gupta*, and Vivek Srikumar
Published at NAACL 2021 [Paper] [Project Page] [Code] [Video] [Poster] [PPT]

InfoTabS: Inference on Tables as Semi-structured Data,
Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar
Published at ACL 2020 [Paper] [Project Page] [Video] [Data] [Code]

IndicSemParse: Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
Divyanshu Aggarwal*, Vivek Gupta*, Anoop Kunchukuttan
To appear at NLP4ConvAI 2023 Project Page, Preprint

IndicXNLI: Evaluating Multilingual Inference for Indian Languages
Divyanshu Aggarwal*, Vivek Gupta*, Anoop Kunchukuttan
To appear at MIA-2022 (non-archival) Preprint

Logic Driven Classification for Low Resource Settings
Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent
Published at AACL-IJCNLP 2020 Paper

A Logic-Driven Framework for Consistency of Neural Models
Tao Li, Vivek Gupta, Maitrey Mehta, and Vivek Srikumar
Published at EMNLP-IJCNLP 2019 Paper

Unbiasing Review Ratings with Tendency-based Collaborative Filtering
Pranshi Yadav*, Priya Yadav*, Pegah Nokhiz, Vivek Gupta
Published at AACL-IJCNLP SRW 2020 Paper

User Bias Removal in Review Score Prediction
Rahul Wadbude, Vivek Gupta, Dheeraj Mekala, Harish Karnick
Published at CoDS-COMAD 2018 and DAB@CIKM 2017 Paper

Equalizing Recourse across Groups
Vivek Gupta*, Pegah Nokhiz*, Chitradeep Dutta Roy*, Suresh Venkatasubramanian
Technical Report. Preprint

Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
Dhruv Mahajan, Vivek Gupta, Satya Keerthi, Sundararjan Sellamanickam
Technical Report. [Preprint]

Unsupervised Contextualized Document Representation,
Ankur Gupta, Vivek Gupta
Published at SustaiNLP 2021 at EMNLP 2021 workshop. [Paper] [Preprint] [PPT] [Poster] [Video] [Code]

Improving Document Classification with Multi-Sense Embeddings,
Vivek Gupta, Ankit Saw, Pegah Nokhiz, Harshit Gupta, and Partha Talukdar
Published at ECAI 2020 [Paper] [Blog] [Video] [Code]
(extention of NAACL-SRW 2019 work)

P-SIF: Document Embeddings using Partition Averaging
Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha Talukdar
Published at AAAI 2020, Presented at SustaiNLP 2020 [Paper] [Appendix] [PPT] [Poster] [Code] [Blog]

Word Polysemy Aware Document Vector Estimation
Vivek Gupta, Ankit Saw, Harshit Gupta, Pegah Nokhiz and Partha Talukdar
Presented at NAACL-SRW 2019 (non-archival)
(extended version appear at ECAI 2020) [Code]

Sparse Composite Document Vectors using soft clustering over distributional representations
Dheeraj Mekala*, Vivek Gupta*, Bhargavi Paranjape , Harish Karnick
Published at EMNLP 2017. [Paper] [PPT] [Video] [Code]

On Dimensional Linguistic Properties of the Word Embedding Space
Vikas Raunak*, Vaibhav Kumar*, Vivek Gupta and Florian Metze
Presented at ACL-SRW 2019 (non-archival), Published at RepL4NLP 2020 [Paper] [Paper] [Code]

Effective Dimensionality Reduction for Word Embeddings
Vikas Raunak, Vivek Gupta and Florian Metze
Published at RepL4NLP 2019. [Paper] [Poster] [Code]

SumPubMed: Summarization Dataset of PubMed Scientific Articles
Vivek Gupta, Prerna Bharti, Pegah Nokhiz, Harish Karnick
Accepted to appear in ACL-IJCNLP SRW 2021 Preprint Dataset PPT

Unsupervised Semantic Abstractive Summarization
Shibhansh Dohare, Vivek Gupta, Harish Karnick
Published at ACL-SRW 2018 Preprint Paper

Distributional Semantics meet Multi-Label Learning
Vivek Gupta, Rahul Wadbude, Nagararjan Natararjan, Harish Karnick, Prateek Jain, Piyush Rai
Published at AAAI 2019 Paper

Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
Dheeraj Mekala, Vivek Gupta, Purushottam Kar, Harish Karnick
Technical Report. Report

On Long-Tailed Phenomena in Neural Machine Translation,
Vikas Raunak, Siddharth Dalmia, Vivek Gupta, and Florian Metze
Published at EMNLP 2020 (Findings), Presented at SPNLP2020 [Paper] [Code]

Product Classification in E-Commerce using Distributional Semantics
Vivek Gupta, Harish Karnick, Ashendra Bansal, Pradhuman Jhala
Published at COLING 2016 Paper

Assisting Humans to Achieve Optimal Sleep by Changing Ambient Temperature
Vivek Gupta*, Siddhant Mittal*, Sandip Bhaumik, Raj Roy
Published at BIBM 2016 Paper