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Lilian Weng explores building open-domain question answering systems

By PulseAugur Editorial · [1 sources] ·

Lilian Weng's blog post details methods for constructing open-domain question-answering (ODQA) systems, focusing on Transformer-based language models. The post distinguishes ODQA from reading comprehension by highlighting the absence of provided context for factual questions. It also discusses challenges in QA data fine-tuning, where test-set questions or answers may appear in training sets, potentially inflating performance metrics. AI

RANK_REASON Blog post detailing research methods for building ODQA systems.

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Lilian Weng explores building open-domain question answering systems

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  1. Lil'Log (Lilian Weng) TIER_1 English(EN) ·

    How to Build an Open-Domain Question Answering System?

    <!-- A model that is capable of answering any question with regard to factual knowledge can enable many useful applications. This post delves into how we can build an Open-Domain Question Answering (ODQA) system, assuming we have access to a powerful pretrained language model. Bo…

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