Papers by Matei Zaharia
Relevance-guided Supervision for OpenQA with ColBERT (2021.tacl-1)
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| Challenge: | Recent work has focused on learning to retrieve passages for open-domain question answering . if notions of relevance are not tailored to questions, the MRC model will not reliably see the best passages . |
| Approach: | They propose a retrieval model that uses coarse-grained vector representations of questions and passages to adapt it to OpenQA. |
| Outcome: | The proposed system improves OpenQA retrieval on Natural Questions, SQuAD, and TriviaQA. |
Language Models Can Easily Learn to Reason from Demonstrations (2025.findings-emnlp)
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Dacheng Li, Shiyi Cao, Tyler Griggs, Shu Liu, Xiangxi Mo, Eric Tang, Sumanth Hegde, Kourosh Hakhamaneshi, Shishir G Patil, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica
| Challenge: | Large reasoning models (LRMs) tackle complex problems by following long chain-of-thoughts (Long CoT) however, the training techniques and data requirements to elicit Long CoT remain poorly understood. |
| Approach: | They propose to use data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation to elicit Long CoT reasoning. |
| Outcome: | The proposed model can learn Long CoT reasoning through data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation. |
Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs (2024.emnlp-main)
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Krista Opsahl-Ong, Michael Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, Omar Khattab
| Challenge: | Language Model Programs (LMs) require crafting prompts that are jointly effective for all modules. |
| Approach: | They propose a novel algorithm for optimizing language model (LM) prompts for all modules by using program- and data-aware techniques and stochastic mini-batch evaluation functions. |
| Outcome: | The proposed algorithm outperforms baseline optimizers on five of seven diverse LM programs by as high as 13% accuracy. |
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking (2023.findings-acl)
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Keshav Santhanam, Jon Saad-Falcon, Martin Franz, Omar Khattab, Avi Sil, Radu Florian, Md Arafat Sultan, Salim Roukos, Matei Zaharia, Christopher Potts
| Challenge: | Neural information retrieval (IR) systems have progressed rapidly in recent years . many IR benchmarks focus on downstream task accuracy, concealing costs incurred . |
| Approach: | They propose to include efficiency considerations on IR benchmarks to help drive progress . eral et al. propose to incorporate query latency and cost budgets into evaluation . |
| Outcome: | a new study shows that the best IR system varies according to how efficiency considerations are chosen and weighed . the proposed benchmarks would allow for more thorough exploration of possible system designs . |
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (2022.naacl-main)
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| Challenge: | Neural information retrieval (IR) methods encode queries and documents into single vectors, but late interaction models produce multi-vector representations at the granularity of each token. |
| Approach: | They propose a retrieval method that couples an aggressive residual compression mechanism with a denoised supervision strategy to improve the quality and space footprint of late interaction. |
| Outcome: | The proposed retriever improves quality and space footprint of late interaction models while reducing space footprint by 6–10x. |
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems (2024.naacl-long)
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| Challenge: | Evaluating retrieval-augmented generation systems relies on hand annotations for input queries, passages to retrieve, and responses to generate. |
| Approach: | They propose an automated evaluation framework for retrieval-augmented generation (RAG) ARES fine tunes lightweight LLM judges on synthetically generated queries and answers . |
| Outcome: | The proposed framework evaluates RAG systems using only human annotations . it can be used to improve system understanding and create targeted solutions . |
LangProBe: a Language Program Benchmark (2025.findings-emnlp)
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Shangyin Tan, Lakshya A Agrawal, Arnav Singhvi, Liheng Lai, Michael J Ryan, Dan Klein, Omar Khattab, Koushik Sen, Matei Zaharia
| Challenge: | Composing language models into multi-step language programs is a mainstream paradigm for building AI systems, but tradeoffs in this space have only scarcely been studied before. |
| Approach: | They propose a benchmarking tool to evaluate the architectures and optimization strategies for language programs . they find that optimized language programs offer strong cost-quality Pareto improvement . |
| Outcome: | The proposed framework evaluates the impact of program architectures and optimizers on quality and cost. |