<a href="https://voice.pkshatech.com/n/n83343d0e00e0">AI King - Japan&#39;s No.1 Quiz AI Competition and won 3rd place｜PKSHA Delta</a> 2023-01-04
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<li><blockquote>
<img src="https://gyazo.com/763fb9ae8f7e017772460b9ef471e515/thumb/1000" alt="image">
</blockquote>
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<li>Talk about combining searches.
<ul>
<li>I&#39;m combining <a href="/en/DPR">DPR</a> and <a href="/en/BM25">BM25</a>.<ul>
<li>DPR is, in a crude way, <a href="/en/vector%20search">vector search</a>, and BM25 is, in a crude way, &quot;modern TF-IDF.</li>
<li>Vector search is weak for low-frequency words (= proper nouns, technical terms and product names), so combine ordinary search</li>
</ul>
</li>
</ul>
</li>
<li><blockquote>
BM25: Lexical Match Base
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</li>
<li><blockquote>
<a href="/en/Dense%20Passage%20Retriever">Dense Passage Retriever</a> (DPR)... Dense vector-based using pre-trained models
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<ul>
<li>Strong semantic similarity</li>
<li>Missing low-frequency words / Significant performance degradation outside the distribution (OOD)</li>
</ul>
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<li>Very small overlap in search results for both DPR and BM25
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<li>→ good points on both sides.
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<li>All teams are <a href="/en/Retriever-Reader">Retriever-Reader</a> type
<ul>
<li>Adopted <a href="/en/Fusion%20in%20Decoder">Fusion in Decoder</a> as Reader<ul>
<li>Top 100 items</li>
</ul>
</li>
<li><blockquote>
Surveying papers around information retrieval, we found several studies in this context, and seq-to-seq based ones are more accurate than building classifiers on BERT basis
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<ul>
<li>Learned Reranker (<a href="/en/rerank">rerank</a>)</li>
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</li>
</ul>
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</ul>
<a href="/en/PKSHA">PKSHA</a>
<a href="/en/LLM">LLM</a>
<ul>
<li><a href="/en/King%20of%20AI">King of AI</a>
<a href="/en/Quiz%20AI">Quiz AI</a></li>
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Combine Searches