Рет қаралды 14,872
#ai #tech #science
Open Domain Question Answering is one of the most challenging tasks in NLP. When answering a question, the model is able to retrieve arbitrary documents from an indexed corpus to gather more information. REALM shows how Masked Language Modeling (MLM) pretraining can be used to train a retriever for relevant documents in an end-to-end fashion and improves over state-of-the-art by a significant margin.
OUTLINE:
0:00 - Introduction & Overview
4:30 - World Knowledge in Language Models
8:15 - Masked Language Modeling for Latent Document Retrieval
14:50 - Problem Formulation
17:30 - Knowledge Retriever Model using MIPS
23:50 - Question Answering Model
27:50 - Architecture Recap
29:55 - Analysis of the Loss Gradient
34:15 - Initialization using the Inverse Cloze Task
41:40 - Prohibiting Trivial Retrievals
44:05 - Null Document
45:00 - Salient Span Masking
50:15 - My Idea on Salient Span Masking
51:50 - Experimental Results and Ablations
57:30 - Concrete Example from the Model
Paper: arxiv.org/abs/...
Code: github.com/goo...
My Video on GPT-3: • GPT-3: Language Models...
My Video on BERT: • BERT: Pre-training of ...
My Video on Word2Vec: • [Classic] Word2Vec: Di...
Abstract:
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network, requiring ever-larger networks to cover more facts.
To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents.
We demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as interpretability and modularity.
Authors: Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang
Links:
KZfaq: / yannickilcher
Twitter: / ykilcher
Discord: / discord
BitChute: www.bitchute.c...
Minds: www.minds.com/...
Parler: parler.com/pro...
LinkedIn: / yannic-kilcher-488534136
If you want to support me, the best thing to do is to share out the content :)
If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: www.subscribes...
Patreon: / yannickilcher
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2
Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m
Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n