2.1k citations · 2.3k across the 16 of their papers we have counts for
15 papers · 1 filter
Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models
Lin Zheng, Vasilisa Bashlovkina, Timothy Dozat +3
Tokenizer-free language models eliminate the tokenizer step of the language modeling pipeline by operating directly on bytes; patch-based variants further aggregate contiguous byte…
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer
Omer Goldman, Uri Shaham, Dan Malkin +11
To achieve equitable performance across languages, large language models (LLMs) must be able to abstract knowledge beyond the language in which it was learnt. However, the current…
Benchmarking Large Language Model Capabilities for Conditional Generation
Joshua Maynez, Priyanka Agrawal, Sebastian Gehrmann
Pre-trained large language models (PLMs) underlie most new developments in natural language processing. They have shifted the field from application-specific model pipelines to a s…
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann +7
Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task. This is especially the case for languages other tha…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot, Joshua Maynez, Shashi Narayan +6
While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelev…
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
Polina Zablotskaia, Du Phan, Joshua Maynez +3
Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign hi…
Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models
Evgeniia Razumovskaia, Joshua Maynez, Annie Louis +2
Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. We consider whether planning b…
QAmeleon: Multilingual QA with Only 5 Examples
Priyanka Agrawal, Chris Alberti, Fantine Huot +6
The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA). Such annotated datasets however are difficult and c…
Conditional Generation with a Question-Answering Blueprint
Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo +6
The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs ofte…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin +64
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of…
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Shashi Narayan, Gonçalo Simões, Yao Zhao +4
We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding str…
Shatter: An Efficient Transformer Encoder with Single-Headed Self-Attention and Relative Sequence Partitioning
Ran Tian, Joshua Maynez, Ankur P. Parikh
The highly popular Transformer architecture, based on self-attention, is the foundation of large pretrained models such as BERT, that have become an enduring paradigm in NLP. While…
Planning with Learned Entity Prompts for Abstractive Summarization
Shashi Narayan, Yao Zhao, Joshua Maynez +3
We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries…