Papers with MARCO
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. |
LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation (2025.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) exhibit significant biases in evaluation tasks, especially in preferentially rating and favoring self-generated content. |
| Approach: | They propose to simulate two critical phases of retrieval-augmented generation (RAG) frameworks where keyword extraction and factual accuracy take precedence over stylistic elements. |
| Outcome: | The proposed model emulates two critical phases of the retrieval-augmented generation framework. |