Papers by Vineeth Rakesh
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025.acl-long)
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Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou
| Challenge: | Existing RAG systems struggle with the quality of retrieval documents, causing performance degradation and reducing performance. |
| Approach: | They propose a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents. |
| Outcome: | The proposed framework outperforms existing RAG frameworks in QA benchmarks and shows superior answer consistency and answer accuracy over baseline methods. |
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) extends large language models with external knowledge, but it must balance limited effective context, redundant retrieved evidence, and the loss of fine-grained facts. |
| Approach: | They propose a hybrid RAG framework that uses natural-language snippets and semantic compression vectors to preserve passages in text form and compress remaining evidence into interpretable vectors for iterative evidence reranking. |
| Outcome: | The proposed framework improves answer relevance, answer correctness and semantic similarity across 9 datasets and 5 open-source LLMs. |
Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis (2023.findings-eacl)
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| Challenge: | Recent advances in the area of long document matching have primarily focused on using transformer-based models for long document encoding and matching. |
| Approach: | They propose to use simple neural models and simple embeddings to improve document matching by taking significantly less training time, energy, and memory. |
| Outcome: | The proposed models outperform the more complex BERT-based models while taking significantly less training time, energy, and memory. |