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Found 18 papers, 10 papers with code
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Learning effective representations of sentences is one of the core missions of natural language understanding.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
DisSent: Learning Sentence Representations from Explicit Discourse Relations
Learning effective representations of sentences is one of the core missions of natural language understanding.
DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain
However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free text notes.
Giving Feedback on Interactive Student Programs with Meta-Exploration
However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs.
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
Contemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games.
LitGen: Genetic Literature Recommendation Guided by Human Explanations
In collaboration with the Clinical Genomic Resource (ClinGen)---the flagship NIH program for clinical curation---we propose the first machine learning system, LitGen, that can retrieve papers for a particular variant and filter them by specific evidence types used by curators to assess for pathogenicity.
Learning to Explain: Answering Why-Questions via Rephrasing
We demonstrate that our strategy is sufficient to generate highly plausible explanations for general open-domain phenomena compared to other models trained on different datasets.
Pragmatic Issue-Sensitive Image Captioning
Image captioning systems have recently improved dramatically, but they still tend to produce captions that are insensitive to the communicative goals that captions should meet.
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
We compare the performance of our model with several baselines in a challenging cross-hospital setting with substantial domain shift.
From Information Bottleneck To Activation Norm Penalty
Recently, a regularization method has been proposed to optimize the variational lower bound of the Information Bottleneck Lagrangian.
Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition
The principle of compositionality has deep roots in linguistics: the meaning of an expression is determined by its structure and the meanings of its constituents.
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process
Contemporary coding education often present students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games.
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Inspired by statistical model selection methods for supervised learning, we introduce a task- and method-agnostic pipeline for automatically training, comparing, selecting, and deploying the best policy when the provided dataset is limited in size.
Model-based Offline Reinforcement Learning with Local Misspecification
We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we propose an empirical algorithm for optimal offline policy selection.
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction.
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
We introduce a new benchmark, LLF-Bench (Learning from Language Feedback Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to interactively learn from natural language feedback and instructions.
