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Found 29 papers, 14 papers with code
Overview of the SustaiNLP 2020 Shared Task
We describe the SustaiNLP 2020 shared task: efficient inference on the SuperGLUE benchmark (Wang et al., 2019).
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69. 5%.
Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning
Large Language Models (LLMs) are vulnerable to `Jailbreaking' prompts, a type of attack that can coax these models into generating harmful and illegal content.
JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation
With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation from scratch.
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
In this work, we take a wider view and explore scalable estimates of data quality that can be used to systematically measure the quality of pretraining data.
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.
Ranked #1 on
Text-to-Music Generation
on MusicCaps
GLARE: A Dataset for Traffic Sign Detection in Sun Glare
It provides an essential enrichment to the widely used LISA Traffic Sign dataset.
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
We present the results of the NLP Community Metasurvey.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains -- or by using approximate heuristics to extract them from everyday text -- which frequently yields unfaithful summaries.
Benchmarking Active Learning Strategies for Materials Optimization and Discovery
In these systems, machine learning controls experiment design, execution, and analysis in a closed loop.
A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
QuestEval: Summarization Asks for Fact-based Evaluation
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.
Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
On the quantum hardware, the quantum variational classifier method has shown promising discrimination power, comparable to that on the quantum simulator.
Quantum Physics High Energy Physics - Experiment
Label Representations in Modeling Classification as Text Generation
Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate.
Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
QAGS is based on the intuition that if we ask questions about a summary and its source, we will receive similar answers if the summary is factually consistent with the source.
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks.
A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
We investigate this problem by proposing a generalized model of sequence generation that unifies decoding in directed and undirected models.
What do you learn from context? Probing for sentence structure in contextualized word representations
The jiant toolkit for general-purpose text understanding models
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks.
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling
Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.
On Measuring Social Biases in Sentence Encoders
The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017).
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
We show that BERT (Devlin et al., 2018) is a Markov random field language model.
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset.
Ranked #46 on
Natural Language Inference
on MultiNLI
Natural Language Inference
Natural Language Understanding
+2
Clustering Stable Instances of Euclidean k-means
We design efficient algorithms that provably recover the optimal clustering for instances that are additive perturbation stable.
Clustering Stable Instances of Euclidean k-means.
To address this disconnect, we study the following question: what properties of real-world instances will enable us to design efficient algorithms and prove guarantees for finding the optimal clustering?
