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PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development

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arxiv 2301.09715 v2 pith:2TNJEYUB submitted 2023-01-23 cs.CL cs.IRcs.LG

classification cs.CLcs.IRcs.LG
keywords primeqamethodsquestionansweringrepositorysotastate-of-the-artadvent
verification ladder T0 review T1 audit T2 compute T3 formal
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The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers. In this paper, we introduce PRIMEQA: a one-stop and open-source QA repository with an aim to democratize QA re-search and facilitate easy replication of state-of-the-art (SOTA) QA methods. PRIMEQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question generation.It has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on pub-lic benchmarks, and expanding pre-existing methods. PRIMEQA is available at : https://github.com/primeqa.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval

    cs.IR 2024-12 conditional novelty 4.0 of 10

    A two-phase fine-tuning pipeline (global contrastive pretraining, then domain-specific hard-negative training) improves LLaMA-based dense retrieval on a Gemini-generated Japanese legal dataset and on a subset of MS MARCO.

  2. Optimizing Multi-Stage Language Models for Effective Text Retrieval

    cs.IR 2024-12 reject novelty 3.0 of 10

    A language-model-only, two-phase retrieval pipeline with hard-negative training and a grid-searched ensemble is reported to outperform sparse, dense, and generative baselines on a Japanese legal retrieval test set and...

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