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\title[Neural Retrieval]{Neural Retrieval at Web Scale \\ Ideas, Trade-offs, and Open Problems}
\author{First Last}
\institute[Example U]{Department of Computer Science \\ University of Example}
\date{\today}

\begin{document}

\frame{\titlepage}

\section{Introduction}
\begin{frame}{Why Retrieval?}
\begin{itemize}
  \item Retrieval powers search, RAG pipelines, and recommendation.
  \item Classical lexical methods (BM25) remain hard baselines.
  \item Dense methods enable semantic matching.
\end{itemize}
\end{frame}

\section{Background}
\begin{frame}{From Sparse to Dense}
\begin{itemize}
  \item Sparse: BM25, TF-IDF --- exact term match.
  \item Dense: bi-encoders producing vector embeddings.
  \item Hybrid: late fusion of scores or first-stage lexical retrieval + reranker.
\end{itemize}
\end{frame}

\section{Method}
\begin{frame}{Our Approach}
A two-stage pipeline:
\begin{enumerate}
  \item Lexical retrieval of top-$k$ candidates.
  \item Cross-encoder reranking with a distilled model.
\end{enumerate}
Knobs: $k$ (recall vs. cost), quantization (latency vs. accuracy), pruning.
\end{frame}

\section{Results}
\begin{frame}{Benchmarks}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule
System & nDCG@10 & Latency (ms) \\
\midrule
BM25             & 0.42 &  8 \\
Dense bi-encoder & 0.48 & 22 \\
Ours (hybrid)    & 0.56 & 35 \\
\bottomrule
\end{tabular}
\end{table}
\end{frame}

\section{Conclusion}
\begin{frame}{Takeaways}
\begin{itemize}
  \item Hybrid still wins on quality per dollar.
  \item Query-time efficiency matters as much as offline accuracy.
  \item The next frontier: end-to-end distillation.
\end{itemize}
\end{frame}

\begin{frame}{Thank You}
\centering
\Huge Questions?\\[0.8em]
\normalsize [email protected]
\end{frame}

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