Semanlink - Arxiv Doc AND NLP@Meta

[2310.01352] RA-DIT: Retrieval-Augmented Dual Instruction Tuning

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2023-10-07 A propos

[2308.13418] Nougat: Neural Optical Understanding for Academic Documents

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2023-09-17 A propos

[2305.07185] MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers

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2023-07-01 A propos

[2303.14177] Scaling Expert Language Models with Unsupervised Domain Discovery

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2023-03-27 A propos

[2212.09741] One Embedder, Any Task: Instruction-Finetuned Text Embeddings

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2023-02-17 A propos

[2302.04761] Toolformer: Language Models Can Teach Themselves to Use Tools

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> Toolformer, **a model
trained to decide which APIs to call, when to
call them, what arguments to pass, and how to
best incorporate the results into future token
prediction**.

> fulfills the
following desiderata:
> - The use of tools should be learned in a
self-supervised way without requiring large
amounts of human annotations
>- The LM should be able to decide for itself when
and how to use which tool.

> Approach based
on the recent idea of using large LMs with incontext
learning (Brown et al., 2020) to generate
entire datasets from scratch.
>
> Given just a handful of human-written examples
of how an API can be used, 
> - we let a LM annotate
a huge language modeling dataset with potential
API calls. 
> - We then use a self-supervised loss to
determine which of these API calls actually help
the model in predicting future tokens. 
>- Finally, we
finetune the LM itself on the API calls that it considers
useful.

[Jay Hack @mathemagic1an sur twitter](https://twitter.com/mathemagic1an/status/1624870248221663232):

> from a small seed set of human inputs (essentially demonstrating usage of APIs), the training set for this behavior is generated by the LLM itself.
> 
> So what does this mean? We've found a promising way to tightly integrate arbitrary APIs with our best-performing models.

2023-02-13 A propos

[1904.04458] Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

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Knowledge Augmented
Language Model (KALM)

a language
model with access to information available in a
KB, no assumptions
about the availability of additional components
(such as Named Entity Taggers) or annotations

> While classes of
named entities (e.g., person or location) occur frequently,
each individual name (e.g, Atherton or
Zhouzhuang) may be observed infrequently even
in a very large corpus of text. As a result language
models learn to represent accurately only the most
popular named entities

> knowing that Alice is a name
used to refer to a person should give ample information
about the context in which the word may
occur (e.g., Bob visited Alice).

> ---

> extends a traditional **RNN LM**

> we enhance a traditional LM with a
gating mechanism that controls whether a particular
word is modeled as a general word or as a reference
to an entity
>
> We train the model end-to-end
with only the traditional predictive language modeling
perplexity objective
>
> KALM is trained end-to-end using
a predictive objective on large corpus of text.

> To the best of our knowledge, KALM is the first
unsupervised neural NER approach.

> KALM extends a traditional, RNN-based neural
LM.

2022-08-31 A propos

[2012.15156] A Memory Efficient Baseline for Open Domain Question Answering

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2022-08-08 A propos

[2208.03299] Few-shot Learning with Retrieval Augmented Language Model

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2022-08-08 A propos

[2206.10658] Questions Are All You Need to Train a Dense Passage Retriever

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> **approach for training dense retrieval models that does not require any labeled training data**. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples.
>
> ART, in contrast, only requires access to unpaired inputs and outputs (e.g. questions and potential answer documents).
>
> It uses a new document-retrieval autoencoding scheme, where
> 1. an input question is used to retrieve a set of evidence documents, and
> 2. the documents are then used to compute the probability of reconstructing the original question.
>
> Training for retrieval based on question reconstruction enables effective unsupervised learning of both document and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning.

[Tweet](doc:2022/07/devendra_singh_sachan_sur_twitt)

> Given an
input question, ART first retrieves a small set
of possible evidences documents. It then recon
structs
the original question by attending to these
documents
>
> The
key idea in ART is to consider the retrieved documents
as a noisy representation of the original
question and question reconstruction probability
as a way of denoising that provides soft-labels for
how likely each document is to have been the correct
result

Refers to [[IZACARD 2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering](doc:2020/12/2012_04584_distilling_knowled)

2022-07-06 A propos

[2009.02252] KILT: a Benchmark for Knowledge Intensive Language Tasks

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2022-01-23 A propos

[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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2022-01-19 A propos

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 A propos

[1906.04980] Unsupervised Question Answering by Cloze Translation

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2021-12-08 A propos

[1911.02116] Unsupervised Cross-lingual Representation Learning at Scale

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2021-07-29 A propos

[2004.04906] Dense Passage Retrieval for Open-Domain Question Answering

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2021-06-03 A propos

[2104.14690] Entailment as Few-Shot Learner

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2021-05-03 A propos

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 A propos

[2010.02194] Self-training Improves Pre-training for Natural Language Understanding

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2021-03-12 A propos

[2010.00904] Autoregressive Entity Retrieval

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2021-01-14 A propos

[2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering

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> a method to train an information retrieval module for downstream tasks, **without using pairs of queries and documents as annotations**.

Uses two models (standard pipeline for open-domain QA):

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 A propos

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 A propos

[2006.15020] Pre-training via Paraphrasing

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2020-06-30 A propos

[1911.03814] Scalable Zero-shot Entity Linking with Dense Entity Retrieval

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2020-05-02 A propos

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

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2019-12-20 A propos

[1912.01412] Deep Learning for Symbolic Mathematics

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2019-12-09 A propos

[1905.11852] EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction

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2019-12-05 A propos

[1911.01464] Emerging Cross-lingual Structure in Pretrained Language Models

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2019-11-06 A propos

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 A propos

[1909.01066] Language Models as Knowledge Bases?

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2019-09-05 A propos

[1907.05242] Large Memory Layers with Product Keys

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> **a structured memory which can be easily integrated into a neural network.** The memory is very large by design and therefore significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead. Its design and access pattern is based on **product keys**, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time.

> a key-value memory layer that can increase model capacity for a negligible computational cost. A 12-layer transformer with a memory outperforms a 24-layer transformer, and is 2x faster!

[Implementation](/doc/2019/08/product_key_memory_pkm_minima)

TODO: compare with [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_)

2019-07-13 A propos

[1503.08895] End-To-End Memory Networks

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