@inproceedings{kirtania-etal-2025-stackfeed,
title = "{STACKFEED}: Structured Textual Actor-Critic Knowledge base editing with {FEED}back",
author = "Kirtania, Shashank and
Gupta, Naman and
Gupta, Priyanshu and
Gulwani, Sumit and
Iyer, Arun and
Iyengar, Suresh Parthasarathy and
Radhakrishna, Arjun and
Rajamani, Sriram K. and
Soares, Gustavo",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.176/",
pages = "2588--2606",
ISBN = "979-8-89176-333-3",
abstract = "Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with Feedback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document-specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified Python packages, and factual question-answering tasks."
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<abstract>Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with Feedback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document-specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified Python packages, and factual question-answering tasks.</abstract>
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%0 Conference Proceedings
%T STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback
%A Kirtania, Shashank
%A Gupta, Naman
%A Gupta, Priyanshu
%A Gulwani, Sumit
%A Iyer, Arun
%A Iyengar, Suresh Parthasarathy
%A Radhakrishna, Arjun
%A Rajamani, Sriram K.
%A Soares, Gustavo
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F kirtania-etal-2025-stackfeed
%X Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with Feedback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document-specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified Python packages, and factual question-answering tasks.
%U https://aclanthology.org/2025.emnlp-industry.176/
%P 2588-2606
Markdown (Informal)
[STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback](https://aclanthology.org/2025.emnlp-industry.176/) (Kirtania et al., EMNLP 2025)
ACL
- Shashank Kirtania, Naman Gupta, Priyanshu Gupta, Sumit Gulwani, Arun Iyer, Suresh Parthasarathy Iyengar, Arjun Radhakrishna, Sriram K. Rajamani, and Gustavo Soares. 2025. STACKFEED: Structured Textual Actor-Critic Knowledge base editing with FEEDback. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2588–2606, Suzhou (China). Association for Computational Linguistics.