80 citations · 251 across the 17 of their papers we have counts for
13 papers · 1 filter
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski +2
Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-cr…
How Context Affects Language Models' Factual Predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus +4
When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for…
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus +9
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. Howe…
How Decoding Strategies Affect the Verifiability of Generated Text
Luca Massarelli, Fabio Petroni, Aleksandra Piktus +5
Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and…
RTFM: Generalising to Novel Environment Dynamics via Reading
Victor Zhong, Tim Rocktäschel, Edward Grefenstette
Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy l…
Language Models as Knowledge Bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis +4
Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models…