Semanlink - AI teams ; Question Answering AND Language Model

1. synthetic queries
are generated for each passage from the target corpus (using an existing pre-trained [T5](tag:text_to_text_transfer_transformer)
encoder-decoder)
2. the generated queries are used for mining negative
passages (retrieving the most similar
paragraphs using an existing dense retrieval
model == hard negatives!)
3. the query-passage pairs are labeled by a cross-encoder and used to train the domain-adapted
dense retriever (using method described in [Hofstätter et al.,
2020](doc:2021/12/2010_02666_improving_efficien))

[Nils Reimers sur Twitter](doc:2021/12/nils_reimers_sur_twitter_do_), [GitHub](https://github.com/UKPLab/gpl),  by the author of [TSDAE](doc:2021/09/2104_06979_tsdae_using_trans)

Claims to improve "Doc2Query" [Document Expansion by Query Prediction](doc:2022/01/1904_08375_document_expansion): ([src](https://twitter.com/KexinWang2049/status/1471435779415150598))

> - GPL: Uses doc2query to construct synthetic data and does knowledge distillation (i.e. training) on that data.
> - Doc2query: Generates queries to extend the documents and use BM25 on top of them w/o training.

2021-12-15 About

How to Fine-Tune Sentence-BERT for Question Answering | Capital One

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2021-11-21 About

[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 About

Which flavor of BERT should you use for your QA task? | by Olesya Bondarenko | Towards Data Science

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2020-10-04 About

[2004.07202] Entities as Experts: Sparse Memory Access with Entity Supervision

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2020-07-11 About

Hugging Face sur Twitter : DistilBERT-cased for Question Answering w/ just 3 lines of javascript

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2020-02-14 About

How Much Knowledge Can You Pack Into the Parameters of a Language Model?

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> It has recently been observed that neural language
models trained on unstructured text can
implicitly store and retrieve knowledge using
natural language queries.

indeed, cf. Facebook's paper [Language Models as Knowledge Bases?](/doc/2019/09/_1909_01066_language_models_as)

> In this short paper,
we measure the practical utility of this
approach by fine-tuning pre-trained models to
answer questions without access to any external
context or knowledge.

> we show that a large language
model pre-trained on unstructured text can
attain competitive results on open-domain question
answering benchmarks without any access
to external knowledge

BUT:

>1. state-of-the-art results only with the largest model
which had 11 billion parameters.
>1. “open-book” models
typically provide some indication of what information
they accessed when answering a question
that provides a useful form of interpretability.
In contrast, our model distributes knowledge
in its parameters in an inexplicable way, which
precludes this form of interpretability.
>1. **the maximum-likelihood objective provides no guarantees as to whether
a model will learn a fact or not.**

So, what's the point? To be compared with this [IBM's paper](/doc/2019/09/_1909_04120_span_selection_pre): "a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself"

2020-02-11 About

Adam Roberts sur Twitter : "New preprint: How Much Knowledge Can You Pack into the Parameters of a Language Model?..."

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2020-02-11 About

[1909.04120] Span Selection Pre-training for Question Answering

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> a **new pre-training task inspired by reading
comprehension** and an **effort to avoid encoding general knowledge in the transformer network itself**

Current transformer architectures store general knowledge -> large models, long pre-training time. Better to offload the requirement of general knowledge to a sparsely activated network.

"Span selection" as an additional auxiliary task: the query is a sentence drawn from a corpus
with a term replaced with a special token: [BLANK]. The term replaced by the blank is the answer term. The passage is
relevant as determined by a BM25 search, and answer-bearing (containing the answer
term). Unlike BERT’s cloze task, where the answer must be drawn from the model itself, the answer is found in a passage
using language understanding.

> **We hope to progress to a model of general purpose language modeling that uses an indexed long
term memory to retrieve world knowledge, rather than holding it in the densely activated transformer encoder layers.**

2019-09-18 About