Papers by Priyanshu Gupta
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. |
TSTR: Target Similarity Tuning Meets the Real World (2023.findings-emnlp)
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| Challenge: | Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs). |
| Approach: | They propose to use sentences from a larger language model to improve similarity between two NL inputs and associated code outputs. |
| Outcome: | The proposed model can be trained on a small number of training examples and is cost-effective. |
STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback (2025.emnlp-industry)
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Shashank Kirtania, Naman Gupta, Priyanshu Gupta, Sumit Gulwani, Arun Iyer, Suresh Parthasarathy Iyengar, Arjun Radhakrishna, Sriram K. Rajamani, Gustavo Soares
| Challenge: | Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. |
| Approach: | They propose a framework that iteratively refines the knowledge base based on expert feedback . they define a ReACT actor agent on each document to perform structured edits . |
| Outcome: | The proposed framework improves the quality and performance of the RAG system on low-resource programming problems, modified Python packages, and factual question-answering tasks. |
MetaReflection: Learning Instructions for Language Agents using Past Reflections (2024.emnlp-main)
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Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani, Arjun Radhakrishna, Gustavo Soares, Sherry Shi
| Challenge: | Large Language Models (LLMs) have gained popularity due to their ability to generate human-like text and solve complex tasks. |
| Approach: | They propose an offline reinforcement learning technique that augments a semantic memory based on experiential learnings from past trials. |
| Outcome: | The proposed technique boosts Language agents’ performance by 4 % to 16.82 % over the raw GPT-4 baseline and performs on par with existing state-of-the-art prompt optimization techniques while requiring fewer LLM calls. |