Papers by Anubhav Shrimal
MARCO: Multi-Agent Real-time Chat Orchestration (2024.emnlp-industry)
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Anubhav Shrimal, Stanley Kanagaraj, Kriti Biswas, Swarnalatha Raghuraman, Anish Nediyanchath, Yi Zhang, Promod Yenigalla
| Challenge: | MARCO is a multi-agent real-time chat orchestration framework for automating workflows that require interactions with tools, reasoning, and human collaboration. |
| Approach: | They propose a multi-agent real-time chat orchestration framework for automating workflows using LLMs. |
| Outcome: | The proposed framework performs with 94.48% accuracy and 92.74% accuracy on restaurant and retail conversations datasets and 44.91% improved latency and 33.71% cost reduction in a production setting. |
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension (2022.naacl-industry)
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| Challenge: | Named Entity Recognition (NER) is a task of locating and classifying entities mentioned in unstructured text into predefined categories. |
| Approach: | They propose to use a BERT-based multi-question MRC task where multiple questions (one question per entity) are considered at the same time for a single text. |
| Outcome: | The proposed architecture leads to 2.5 times faster training and 2.3 times faster inference on three NER datasets. |
PARSE: LLM Driven Schema Optimization for Reliable Entity Extraction (2025.emnlp-industry)
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| Challenge: | Structured information extraction from unstructured text is critical for Software 3.0 systems . current approaches to extract structured information from unstructed text are static contracts . |
| Approach: | They propose a system that automates JSON schemas for LLM consumption and optimizes them for LRM consumption. |
| Outcome: | The proposed system improves extraction accuracy and reduces errors by 92% within the first retry and maintaining practical latency. |