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@jobergum - Arxiv Doc - ColBERT - Discuté avec Raphaël - Domain specific corpora - EMNLP 2021 - Entity Retrieval - ESWC - Gautier Izacard - GitHub project - Hard negative mining - Information retrieval - KG-aware low-resource learning - Knowledge-augmented Language Models - Knowledge distillation - Knowledge Extraction - Knowledge-Intensive NLP Tasks - Knowledge management - Language Models + Knowledge - Laure Soulier - LLM - LM + Search - Long documents - Lynda Tamine - Michael Glass - Natural language generation - Neural Search - NLP@ENS - NLP@IBM - NLP@Meta - NLP@Stanford - NLP based IR - Not Encoding Factual Knowledge in Language Model - Omar Khattab - Personal assistant - Question Answering - Retrieval augmented LM - Retriever-Reader - Slides - Slot filling - Thèse IRIT-Renault: biblio initiale - Tweet - Word + Entity embeddings - Word sense / Lexical ambiguity - Zero shot -

AI + Knowledge Bases ; Information retrieval AND NLP tasks / problems

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15 Documents

Announcing Vespa Long-Context ColBERT | Vespa Blog

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2024-03-03 About

Ravi Theja sur X : "𝐀𝐜𝐭𝐢𝐯𝐞𝐑𝐀𝐆: 𝐑𝐞𝐯𝐞𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐨𝐟 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐯𝐢𝐚 𝐀𝐜𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠..."

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2024-02-25 About

long document ranking · Issue #11 · stanford-futuredata/ColBERT

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2024-01-31 About

stanfordnlp/dspy: 𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.

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2023-02-18 About

deepset sur Twitter : " Generative models have taken the world of NLP by storm. But LLMs do not know about your personal data. This makes personal assistants, enterprise knowledge management and many other applications challenging. Retrieval augmented pipelines are the answer"

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2023-02-02 About

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..."

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2022-12-21 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 About

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 About

Recent Advances in Retrieval-Augmented Text Generation

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2022-07-11 About

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 About

Integrate ORQA and REALM for Open Domain Question Answering · Issue #312 · deepset-ai/haystack

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2022-01-12 About

[2112.01488] ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

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2021-12-05 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

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

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2020-05-02 About

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 About