Semanlink - Information retrieval ; AI + Knowledge AND NLP@Meta

Akari Asai sur Twitter : "Can we solely rely on LLMs’ memories (eg replace search w ChatGPT)? Probably not... Our analysis shows how retrieval is complementary to LLMs’ parametric knowledge..."

Tags:

2022-12-21 A propos

Hugging Face sur Twitter : "Transformers release of the Retrieval-Augmented Generation model in collaboration with @facebookai!"

Tags:

2021-02-23 A propos

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

Tags:

> 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

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

Tags:

2020-05-02 A propos

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

Tags:

2019-12-20 A propos