Papers by Abhinav Gupta
Generating Contextual Images for Long-Form Text (2024.lrec-main)
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| Challenge: | Recent advances in Text-to-Image models require short prompts that describe both the content and style of the target image. |
| Approach: | They propose to use Large Language Models (LLMs) and Text-to-Image Models to synthesize relevant visual imagery from generic long-form text. |
| Outcome: | The proposed models can generate high-quality images from short prompts that describe both the content and style of the target image. |
GETReason: Enhancing Image Context Extraction through Hierarchical Multi-Agent Reasoning (2025.acl-long)
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| Challenge: | Existing methods for analyzing images of events fail to accurately extract contextual meaning from images. |
| Approach: | They propose a framework to infer global event, temporal, and geospatial information from images . they also introduce a new metric GREAT for a reasoning-weighted evaluation . |
| Outcome: | The proposed framework enables better understanding of event significance from images . it also shows that meaningful information can be inferred from images, allowing them to be effectively linked to their corresponding events and contextual background. |
Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction (2026.acl-industry)
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| Challenge: | Existing approaches to define customer intents or contact reasons are fragmented and manual . existing systems fail to capture the linguistic diversity of thousands of daily callers . |
| Approach: | They propose a framework that develops a hierarchical Reason-of-Contact taxonomy . it covers hundreds of business processes and can be deployed in real time . |
| Outcome: | The proposed framework achieves 10% improvement in F1 score over baseline approaches on a reference dataset. |
Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations (2026.findings-acl)
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| Challenge: | Empirical studies suggest that comprehending action, perceptual and abstract concepts elicits rapid, automatic activity in modality-specific brain areas. |
| Approach: | They propose a model that predicts Lancaster sensorimotor norms from word lexical embeddings. |
| Outcome: | The proposed model predicts Lancaster sensorimotor norms from word lexical embeddings. |
CISLR: Corpus for Indian Sign Language Recognition (2022.emnlp-main)
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Abhinav Joshi, Ashwani Bhat, Pradeep S, Priya Gole, Shashwat Gupta, Shreyansh Agarwal, Ashutosh Modi
| Challenge: | Existing work on natural language processing has shown promising improvements in text classification, translation and generation in widely used spoken languages. |
| Approach: | They propose a new Indian Sign Language corpus for word-level recognition using videos . they propose CISLR model that leverages resource rich American Sign Language to learn generalized features for improving Indian Sign language predictions. |
| Outcome: | The proposed model improves word recognition in Indian Sign Language using video . it leverages resource rich American Sign Language to learn generalized features . |
Evaluating AI for Finance: Is AI Credible at Assessing Investment Risk Appetite? (2025.emnlp-industry)
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Divij Chawla, Ashita Bhutada, Duc Anh Do, Abhinav Raghunathan, Vinod Sp, Cathy Guo, Dar Win Liew, Prannaya Gupta, Rishabh Bhardwaj, Rajat Bhardwaj, Soujanya Poria
| Challenge: | Our analysis was conducted on proprietary systems and open-weight models . FINRISKEVAL analyzed 1,720 profiles spanning a broad spectrum of possible risk categories . |
| Approach: | They evaluated proprietary AI systems and open-weight models to assess investment risk appetite using carefully curated user profiles. |
| Outcome: | The proposed models exhibit significant variance when user attributes that should not influence risk computation are changed. |
AUTOSUMM: A Comprehensive Framework for LLM-Based Conversation Summarization (2025.acl-industry)
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Abhinav Gupta, Devendra Singh, Greig A Cowan, N Kadhiresan, Siddharth Srivastava, Yagneswaran Sriraja, Yoages Kumar Mantri
| Challenge: | Large language models (LLMs) are used to summarize large volumes of textual information into a smaller, more manageable size. |
| Approach: | They propose a large language model-based summarization system for regulated banking environments that generates accurate, privacy-compliant summaries of customer-advisor conversations. |
| Outcome: | The proposed system achieves 94% factual consistency rate and significant reduction in hallucination rate. |
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark (2021.eacl-main)
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| Challenge: | Existing datasets for task-oriented dialog systems are limited and expensive . current models are based on the simple intent and slot detection paradigm for non-compositional queries. |
| Approach: | They propose to use a multilingual dataset to scale semantic parsing models to new languages . they demonstrate an average improvement of +6.3 points on Slot F1 for existing datasets . |
| Outcome: | The proposed model achieves an average improvement of +6.3 points on Slot F1 over existing models. |
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue (2022.naacl-main)
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| Challenge: | Recent work has leveraged natural language descriptions of schema elements to enable universal dialogue systems; however, descriptions only indirectly convey schema semantics. |
| Approach: | They propose to use schema-guided modeling to prompt seq2seq models with a labeled example dialogue to show schema semantics rather than tell them. |
| Outcome: | The proposed model outperforms models using short examples as schema representations on two popular dialogue state tracking benchmarks. |
Can Large Language Models Infer Human Actions and Motives? Evaluation in Social Prediction and Inspection Games (2026.findings-acl)
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| Challenge: | Game theory provides a framework for studying human behaviors through incentivized games that simulate social situations. |
| Approach: | They used two validated games from the cognitive science literature to study how well several recent open- and closed-source LLMs predict player actions with underlying human motives. |
| Outcome: | The results show that state-of-the-art LLMs can achieve accuracy close to human levels in predicting players’ actions with underlying human motives in SPGs, but failed to recognize statistical patterns in players’ action. |
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)
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| Challenge: | Code-switching (CS) is the alternation of languages within an utterance or conversation. |
| Approach: | They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available. |
| Outcome: | The proposed models improve when only English data is available alongside zero or a few CS training instances. |
Seeded self-play for language learning (D19-64)
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| Challenge: | Current methods for learning human language are too data inefficient to learn it in this way. |
| Approach: | They propose to train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. |
| Outcome: | The proposed algorithm minimizes the number of on-policy interactions while learning human language while minimizing the number on-political interactions. |
AnyTOD: A Programmable Task-Oriented Dialog System (2023.emnlp-main)
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Jeffrey Zhao, Yuan Cao, Raghav Gupta, Harrison Lee, Abhinav Rastogi, Mingqiu Wang, Hagen Soltau, Izhak Shafran, Yonghui Wu
| Challenge: | a neuro-symbolic approach allows zero-shot adaptation to unseen tasks and domains . a neural LM keeps track of events that occur during a conversation and a symbolic program implements dialog policy is executed to recommend actions. |
| Approach: | They propose an end-to-end, zero-shot task-oriented dialog system . it is designed to adapt to unseen tasks or domains without prior training . |
| Outcome: | The proposed system can be programmed to adapt to unseen tasks without training . it reduces data collection and training requirements for enabling new TOD 1 16189 tasks . |