Author Interview - Transformer Memory as a Differentiable Search Index

Yannic Kilcher · Beginner ·📄 Research Papers Explained ·4y ago
#neuralsearch #interview #google This is an interview with the authors Yi Tay and Don Metzler. Paper Review Video: https://youtu.be/qlB0TPBQ7YY Search engines work by building an index and then looking up things in it. Usually, that index is a separate data structure. In keyword search, we build and store reverse indices. In neural search, we build nearest-neighbor indices. This paper does something different: It directly trains a Transformer to return the ID of the most relevant document. No similarity search over embeddings or anything like this is performed, and no external data structure is needed, as the entire index is essentially captured by the model's weights. The paper experiments with various ways of representing documents and training the system, which works surprisingly well! OUTLINE: 0:00 - Intro 0:50 - Start of Interview 1:30 - How did this idea start? 4:30 - How does memorization play into this? 5:50 - Why did you not compare to cross-encoders? 7:50 - Instead of the ID, could one reproduce the document itself? 10:50 - Passages vs documents 12:00 - Where can this model be applied? 14:25 - Can we make this work on large collections? 19:20 - What's up with the NQ100K dataset? 23:55 - What is going on inside these models? 28:30 - What's the smallest scale to obtain meaningful results? 30:15 - Investigating the document identifiers 34:45 - What's the end goal? 38:40 - What are the hardest problems currently? 40:40 - Final comments & how to get started Paper: https://arxiv.org/abs/2202.06991 Abstract: In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifyi
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Chapters (16)

Intro
0:50 Start of Interview
1:30 How did this idea start?
4:30 How does memorization play into this?
5:50 Why did you not compare to cross-encoders?
7:50 Instead of the ID, could one reproduce the document itself?
10:50 Passages vs documents
12:00 Where can this model be applied?
14:25 Can we make this work on large collections?
19:20 What's up with the NQ100K dataset?
23:55 What is going on inside these models?
28:30 What's the smallest scale to obtain meaningful results?
30:15 Investigating the document identifiers
34:45 What's the end goal?
38:40 What are the hardest problems currently?
40:40 Final comments & how to get started
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