Christopher D. Manning

research

∙ 01/26/2023

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

The fluency and factual knowledge of large language models (LLMs) height...

0 Eric Mitchell, et al. ∙

research

∙ 11/27/2022

Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models

A growing ecosystem of large, open-source foundation models has reduced ...

0 Eric Mitchell, et al. ∙

research

∙ 11/21/2022

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

While large pre-trained language models are powerful, their predictions ...

0 Eric Mitchell, et al. ∙

research

∙ 11/16/2022

On Measuring the Intrinsic Few-Shot Hardness of Datasets

While advances in pre-training have led to dramatic improvements in few-...

0 Xinran Zhao, et al. ∙

research

∙ 11/16/2022

Holistic Evaluation of Language Models

Language models (LMs) are becoming the foundation for almost all major l...

21 Percy Liang, et al. ∙

research

∙ 11/07/2022

Fixing Model Bugs with Natural Language Patches

Current approaches for fixing systematic problems in NLP models (e.g. re...

0 Shikhar Murty, et al. ∙

research

∙ 11/02/2022

Characterizing Intrinsic Compositionality in Transformers with Tree Projections

When trained on language data, do transformers learn some arbitrary comp...

0 Shikhar Murty, et al. ∙

research

∙ 10/27/2022

Truncation Sampling as Language Model Desmoothing

Long samples of text from neural language models can be of poor quality....

0 John Hewitt, et al. ∙

research

∙ 10/17/2022

Deep Bidirectional Language-Knowledge Graph Pretraining

Pretraining a language model (LM) on text has been shown to help various...

4 Michihiro Yasunaga, et al. ∙

research

∙ 08/07/2022

When can I Speak? Predicting initiation points for spoken dialogue agents

Current spoken dialogue systems initiate their turns after a long period...

0 Siyan Li, et al. ∙

research

∙ 07/25/2022

Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent

We present Chirpy Cardinal, an open-domain social chatbot. Aiming to be ...

13 Ethan A. Chi, et al. ∙

research

∙ 07/01/2022

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

One concern with the rise of large language models lies with their poten...

0 Peter Henderson, et al. ∙

research

∙ 06/13/2022

Memory-Based Model Editing at Scale

Even the largest neural networks make errors, and once-correct predictio...

8 Eric Mitchell, et al. ∙

research

∙ 05/25/2022

Detecting Label Errors using Pre-Trained Language Models

We show that large pre-trained language models are extremely capable of ...

0 Derek Chong, et al. ∙

research

∙ 02/18/2022

Synthetic Disinformation Attacks on Automated Fact Verification Systems

Automated fact-checking is a needed technology to curtail the spread of ...

0 Yibing Du, et al. ∙

research

∙ 01/21/2022

GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

Answering complex questions about textual narratives requires reasoning ...

1 Xikun Zhang, et al. ∙

research

∙ 12/14/2021

You Only Need One Model for Open-domain Question Answering

Recent works for Open-domain Question Answering refer to an external kno...

0 Haejun Lee, et al. ∙

research

∙ 10/21/2021

Fast Model Editing at Scale

While large pre-trained models have enabled impressive results on a vari...

16 Eric Mitchell, et al. ∙

research

∙ 10/14/2021

Hindsight: Posterior-guided training of retrievers for improved open-ended generation

Many text generation systems benefit from using a retriever to retrieve ...

1 Ashwin Paranjape, et al. ∙

research

∙ 10/05/2021

ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts

Reviewing contracts is a time-consuming procedure that incurs large expe...

0 Yuta Koreeda, et al. ∙

research

∙ 09/19/2021

Conditional probing: measuring usable information beyond a baseline

Probing experiments investigate the extent to which neural representatio...

0 John Hewitt, et al. ∙

research

∙ 07/20/2021

Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning

Although virtual agents are increasingly situated in environments where ...

5 Kaylee Burns, et al. ∙

research

∙ 07/06/2021

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

Active learning promises to alleviate the massive data needs of supervis...

15 Siddharth Karamcheti, et al. ∙

research

∙ 05/01/2021

Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser

While many NLP papers, tasks and pipelines assume raw, clean texts, many...

0 Yuta Koreeda, et al. ∙

research

∙ 04/16/2021

Human-like informative conversations: Better acknowledgements using conditional mutual information

This work aims to build a dialogue agent that can weave new factual cont...

0 Ashwin Paranjape, et al. ∙

research

∙ 12/15/2020

Pre-Training Transformers as Energy-Based Cloze Models

We introduce Electric, an energy-based cloze model for representation le...

0 Kevin Clark, et al. ∙

research

∙ 10/30/2020

SLM: Learning a Discourse Language Representation with Sentence Unshuffling

We introduce Sentence-level Language Modeling, a new pre-training object...

0 Haejun Lee, et al. ∙

research

∙ 10/23/2020

Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text

Current approaches to open-domain question answering often make crucial ...

0 Peng Qi, et al. ∙

research

∙ 10/15/2020

RNNs can generate bounded hierarchical languages with optimal memory

Recurrent neural networks empirically generate natural language with hig...

23 John Hewitt, et al. ∙

research

∙ 10/14/2020

The EOS Decision and Length Extrapolation

Extrapolation to unseen sequence lengths is a challenge for neural gener...

0 Benjamin Newman, et al. ∙

research

∙ 10/02/2020

Contrastive Learning of Medical Visual Representations from Paired Images and Text

Learning visual representations of medical images is core to medical ima...

39 Yuhao Zhang, et al. ∙

research

∙ 08/27/2020

Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations

We present Chirpy Cardinal, an open-domain dialogue agent, as a research...

8 Ashwin Paranjape, et al. ∙

research

∙ 07/29/2020

Biomedical and Clinical English Model Packages in the Stanza Python NLP Library

We introduce biomedical and clinical English model packages for the Stan...

0 Yuhao Zhang, et al. ∙

research

∙ 05/09/2020

Finding Universal Grammatical Relations in Multilingual BERT

Recent work has found evidence that Multilingual BERT (mBERT), a transfo...

0 Ethan A. Chi, et al. ∙

research

∙ 04/30/2020

Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations

We investigate the problem of generating informative questions in inform...

0 Peng Qi, et al. ∙

research

∙ 04/22/2020

Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection

Universal Dependencies is an open community effort to create cross-lingu...

0 Joakim Nivre, et al. ∙

research

∙ 04/18/2020

Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

Question Generation (QG) is fundamentally a simple syntactic transformat...

0 Kaustubh D. Dhole, et al. ∙

research

∙ 04/02/2020

Mapping Three Decades of Intellectual Change in Academia

Research on the development of science has focused on the creation of mu...

0 Daniel Ramage, et al. ∙

research

∙ 03/23/2020

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Masked language modeling (MLM) pre-training methods such as BERT corrupt...

0 Kevin Clark, et al. ∙

research

∙ 03/16/2020

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

We introduce Stanza, an open-source Python natural language processing t...

0 Peng Qi, et al. ∙

research

∙ 11/06/2019

Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports

Neural abstractive summarization models are able to generate summaries w...

0 Yuhao Zhang, et al. ∙

research

∙ 10/15/2019

Answering Complex Open-domain Questions Through Iterative Query Generation

It is challenging for current one-step retrieve-and-read question answer...

0 Peng Qi, et al. ∙

research

∙ 09/24/2019

Do Massively Pretrained Language Models Make Better Storytellers?

Large neural language models trained on massive amounts of text have eme...

2 Abigail See, et al. ∙

research

∙ 07/10/2019

BAM! Born-Again Multi-Task Networks for Natural Language Understanding

It can be challenging to train multi-task neural networks that outperfor...

0 Kevin Clark, et al. ∙

research

∙ 07/09/2019

Learning by Abstraction: The Neural State Machine

We introduce the Neural State Machine, seeking to bridge the gap between...

49 Drew A. Hudson, et al. ∙

research

∙ 06/11/2019

What Does BERT Look At? An Analysis of BERT's Attention

Large pre-trained neural networks such as BERT have had great recent suc...

0 Kevin Clark, et al. ∙

research

∙ 02/25/2019

GQA: a new dataset for compositional question answering over real-world images

We introduce GQA, a new dataset for real-world visual reasoning and comp...

6 Drew A. Hudson, et al. ∙

research

∙ 01/29/2019

Universal Dependency Parsing from Scratch

This paper describes Stanford's system at the CoNLL 2018 UD Shared Task....

0 Peng Qi, et al. ∙

research

∙ 09/26/2018

Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

Dependency trees help relation extraction models capture long-range rela...

0 Yuhao Zhang, et al. ∙

research

∙ 09/25/2018

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Existing question answering (QA) datasets fail to train QA systems to pe...

0 Zhilin Yang, et al. ∙

Christopher D. Manning

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