Papers by Peter Staar
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2025.coling-industry)
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Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
| Challenge: | Retrieval Augmented Generation (RAG) systems are widespread in the industry. |
| Approach: | They propose to use Q&A datasets to assess retrieval performance and label-targeted data generation to refine RAG datasets. |
| Outcome: | The proposed system can generate Q&A datasets with fine-tuned small LLMs. |
pNLP-Mixer: an Efficient all-MLP Architecture for Language (2023.acl-industry)
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| Challenge: | large pre-trained language models are impractical for on-device applications due to their size and inference cost. |
| Approach: | They propose an embedding-free MLP-Mixer model for on-device NLP that achieves high weight-efficiency thanks to a novel projection layer. |
| Outcome: | The proposed model beats state-of-the-art of tiny models by 97.8% on two datasets . it beats mBERT on MTOP and multiATIS, while using 170x less parameters . |
INDUS: Effective and Efficient Language Models for Scientific Applications (2024.emnlp-industry)
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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Lima, Panagiotis Vagenas, S. Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar Lee
| Challenge: | Large language models trained on general domain corpora showed remarkable results on natural language processing tasks. |
| Approach: | They develop a suite of large language models trained on general domain corpora that address NLP tasks and smaller versions of them created using knowledge distillation. |
| Outcome: | The proposed models outperform general-purpose and domain-specific encoders on new and existing tasks and in industrial settings. |
Unsupervised Term Extraction for Highly Technical Domains (2022.emnlp-industry)
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| Challenge: | Term extraction is an important task for knowledge discovery platforms because domain specific terms are the linguistic representation of domainspecific concepts. |
| Approach: | They propose a term extraction subsystem that uses an unsupervised annotator to generate training data to fine-tune transformer models. |
| Outcome: | The proposed system can generalize across domains while reducing latency and inference time while preserving the high performance of the existing system. |