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08/23/2023
Evaluation of Faithfulness Using the Longest Supported Subsequence
As increasingly sophisticated language models emerge, their trustworthin...
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08/11/2023
Self-Alignment with Instruction Backtranslation
We present a scalable method to build a high quality instruction followi...
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05/23/2023
Active Learning Principles for In-Context Learning with Large Language Models
The remarkable advancements in large language models (LLMs) have signifi...
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04/17/2023
LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction
Instruction tuning enables language models to generalize more effectivel...
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02/15/2023
Augmented Language Models: a Survey
This survey reviews works in which language models (LMs) are augmented w...
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02/09/2023
Toolformer: Language Models Can Teach Themselves to Use Tools
Language models (LMs) exhibit remarkable abilities to solve new tasks fr...
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12/19/2022
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Instruction tuning enables pretrained language models to perform new tas...
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11/16/2022
Task-aware Retrieval with Instructions
We study the problem of retrieval with instructions, where users of a re...
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11/15/2022
MEAL: Stable and Active Learning for Few-Shot Prompting
Few-shot classification in NLP has recently made great strides due to th...
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09/27/2022
EditEval: An Instruction-Based Benchmark for Text Improvements
Evaluation of text generation to date has primarily focused on content c...
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08/24/2022
PEER: A Collaborative Language Model
Textual content is often the output of a collaborative writing process: ...
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08/05/2022
Few-shot Learning with Retrieval Augmented Language Models
Large language models have shown impressive few-shot results on a wide r...
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07/08/2022
Improving Wikipedia Verifiability with AI
Verifiability is a core content policy of Wikipedia: claims that are lik...
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03/11/2022
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment
Pretrained language models (PLMs) have achieved superhuman performance o...
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02/12/2022
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
Due to the high costs associated with finetuning large language models, ...
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11/26/2021
True Few-Shot Learning with Prompts – A Real-World Perspective
Prompt-based approaches are strong at few-shot learning. However, Perez ...
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04/15/2021
Generating Datasets with Pretrained Language Models
To obtain high-quality sentence embeddings from pretrained language mode...
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02/28/2021
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
When trained on large, unfiltered crawls from the internet, language mod...
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12/22/2020
Few-Shot Text Generation with Pattern-Exploiting Training
Providing pretrained language models with simple task descriptions or pr...
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10/26/2020
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
A recent approach for few-shot text classification is to convert textual...
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09/15/2020
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
When scaled to hundreds of billions of parameters, pretrained language m...
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01/21/2020
Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference
Some NLP tasks can be solved in a fully unsupervised fashion by providin...
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10/16/2019
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
Pretraining deep contextualized representations using an unsupervised la...
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04/14/2019
Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
Pretraining deep neural network architectures with a language modeling o...
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04/02/2019
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts
Learning high-quality embeddings for rare words is a hard problem becaus...
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11/09/2018
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
Word embeddings are a key component of high-performing natural language ...
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07/24/2017
