Papers by Shashank 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| Challenge: | Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data.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. |
SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories (2024.emnlp-main)
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Ben Bogin, Kejuan Yang, Shashank Gupta, Kyle Richardson, Erin Bransom, Peter Clark, Ashish Sabharwal, Tushar Khot
| Challenge: | Large Language Models (LLMs) have made significant progress in writing code, but can they be used to reproduce results from research repositories? |
| Approach: | They propose a benchmark to evaluate the capability of Large Language Models to reproduce results from research repositories. |
| Outcome: | The benchmark aims to capture the realistic challenges faced by researchers working with machine learning and natural language processing repositories. |
Group, Embed and Reason: A Hybrid LLM and Embedding Framework for Semantic Attribute Alignment (2025.emnlp-industry)
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Shramona Chakraborty, Shashank Mujumdar, Nitin Gupta, Sameep Mehta, Ronen Kat, Itay Etelis, Mohamed Mahameed, Itai Guez, Rachel Tzoref-Brill
| Challenge: | a framework to align attributes that refer to the same concept but differ across schemas is challenging in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and domain-specific terminologies. |
| Approach: | They propose a framework that combines contextual reasoning and embedding-based similarity to address token limitations and hallucinations. |
| Outcome: | The proposed framework scales to large schemas and shows strong performance on healthcare schemas. |
A Study on the Efficiency and Generalization of Light Hybrid Retrievers (2023.acl-short)
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Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
| Challenge: | Recent research focuses on building neural retrievers which learn dense embeddings of query and document into a semantic space. |
| Approach: | They propose to use an indexing-efficient dense retriever to reduce hybrid retrievers' memory by using the state-based indexing algorithm. |
| Outcome: | The proposed hybrid retriever saves 13 memory while maintaining 98.0% performance on out-of-domain datasets and adversarial attacks datasets. |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
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. |