Papers by Naman Gupta
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| Challenge: | Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data.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. |
Map&Make: Schema Guided Text to Table Generation (2025.acl-long)
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| Challenge: | Existing methods for text-to-table generation overlook what complex information to extract and how to infer it from text. |
| Approach: | They propose a method that decomposes text into atomic propositions to infer latent schemas. |
| Outcome: | The proposed method shows significant gains in accuracy and interpretability on three datasets. |
Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives (2026.findings-acl)
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| Challenge: | Large language model (LLM) approaches to tabular summarization rely on prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. |
| Approach: | They propose a diagnostic benchmark for long-context tabular summarization using decomposition pipelines and entity-level intermediate representations. |
| Outcome: | The proposed benchmark improves accuracy and numerical fidelity, but lacks local arithmetic. |