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Favoris ; Education ; Language Model AND Arxiv Doc

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19 Documents

[2404.13950] SPLATE: Sparse Late Interaction Retrieval

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2024-04-23 About

[2404.03592] ReFT: Representation Finetuning for Language Models

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2024-04-08 About

[2307.15936] A Theory for Emergence of Complex Skills in Language Models

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2024-02-24 About

[2302.06600] Task-Specific Skill Localization in Fine-tuned Language Models

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2023-08-25 About

[2305.14788] Adapting Language Models to Compress Contexts

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2023-06-04 About

[2304.09848] Evaluating Verifiability in Generative Search Engines

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2023-04-23 About

[2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

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2023-04-05 About

[2203.14465] STaR: Bootstrapping Reasoning With Reasoning

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2023-02-07 About

[2210.09338] Deep Bidirectional Language-Knowledge Graph Pretraining

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2022-10-23 About

[2208.01066] What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

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2022-09-17 About

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

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2022-08-08 About

[2203.13088] Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

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2022-03-30 About

[2101.12294] Combining pre-trained language models and structured knowledge

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2022-03-25 About

[2006.05987] Revisiting Few-sample BERT Fine-tuning

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2022-03-21 About

[2004.12832] ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

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2022-01-12 About

[2007.00814] Relevance-guided Supervision for OpenQA with ColBERT

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2022-01-07 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

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

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2019-12-20 About