Papers by Sandeep Tata
PRISM: Efficient Long-Range Reasoning With Short-Context LLMs (2025.emnlp-main)
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| Challenge: | Existing solutions to long-range language tasks require large compute budgets and complex task-specific design choices. |
| Approach: | They propose an in-context method that uses structured schemas to generate short-contemporary outputs. |
| Outcome: | a new in-context method outperforms baselines on diverse tasks with 4x shorter contexts . it scales down to tiny contexts without increasing costs or sacrificing quality . |
SUMIE: A Synthetic Benchmark for Incremental Entity Summarization (2025.coling-main)
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| Challenge: | Existing datasets that test incrementally update entity summaries are lacking. |
| Approach: | They propose a fully synthetic dataset that exposes real-world IES challenges by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy. |
| Outcome: | The proposed dataset shows that state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. |
Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models (2023.emnlp-main)
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| Challenge: | a key bottleneck in developing automatic extraction models for visually rich documents is the cost of acquiring labeled documents. |
| Approach: | They propose selective labeling to provide "yes/no" labels for candidate extractions predicted by a model trained on partially labeled documents. |
| Outcome: | The proposed method reduces the cost of acquiring labeled data by 10 with a negligible loss in accuracy. |
Representation Learning for Information Extraction from Form-like Documents (2020.acl-main)
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| Challenge: | Form-like documents like invoices, purchase orders, tax forms and insurance quotes are common in day-to-day business workflows, but current techniques for processing them largely still employ manual effort or brittle and error-prone heuristics for extraction. |
| Approach: | They propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. |
| Outcome: | The proposed system generates extraction candidates based on neighboring words in the document and is interpretable, as shown using loss cases. |
Enhancing Incremental Summarization with Structured Representations (2024.findings-emnlp)
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| Challenge: | Large language models struggle with processing extensive input contexts, leading to redundancy or incoherency. |
| Approach: | They propose a chain-of-key update based on JSON structured memory representations to improve summarization performance by 40% and 14% on two public datasets. |
| Outcome: | The proposed method improves summarization performance by 40% and 14% on two datasets. |