400 citations · 633 across the 18 of their papers we have counts for
6 papers · 1 filter
Evaluation of Faithfulness Using the Longest Supported Subsequence
Anirudh Mittal, Timo Schick, Mikel Artetxe +1
As increasingly sophisticated language models emerge, their trustworthiness becomes a pivotal issue, especially in tasks such as summarization and question-answering. Ensuring thei…
Shepherd: A Critic for Language Model Generation
Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan +7
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shephe…
Active Learning Principles for In-Context Learning with Large Language Models
Katerina Margatina, Timo Schick, Nikolaos Aletras +1
The remarkable advancements in large language models (LLMs) have significantly enhanced the performance in few-shot learning settings. By using only a small number of labeled examp…
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao +6
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augme…
Augmented Language Models: a Survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli +10
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially comple…
Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì +5
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with…