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Found 20 papers, 3 papers with code
Leveraging Large Language Models for Multiple Choice Question Answering
A more natural prompting approach is to present the question and answer options to the LLM jointly and have it output the symbol (e. g., "A") associated with its chosen answer option.
Towards Neural Programming Interfaces
It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models.
Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion.
What can you do with a rock? Affordance extraction via word embeddings
Autonomous agents must often detect affordances: the set of behaviors enabled by a situation.
Embedding Grammars
Classic grammars and regular expressions can be used for a variety of purposes, including parsing, intent detection, and matching.
Nested Reasoning About Autonomous Agents Using Probabilistic Programs
As autonomous agents become more ubiquitous, they will eventually have to reason about the plans of other agents, which is known as theory of mind reasoning.
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages.
Nonparametric Bayesian Policy Priors for Reinforcement Learning
We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations.
Automated Variational Inference in Probabilistic Programming
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs.
Graph Neural Processes: Towards Bayesian Graph Neural Networks
We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes.
Video Extrapolation with an Invertible Linear Embedding
We predict future video frames from complex dynamic scenes, using an invertible neural network as the encoder of a nonlinear dynamic system with latent linear state evolution.
Wasserstein Neural Processes
Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions.
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
We propose using propositional logic to specify the importance of multiple objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads
We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad.
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
Large natural language models (such as GPT-3 or T5) demonstrate impressive abilities across a range of general NLP tasks.
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels
Pre-trained language models derive substantial linguistic and factual knowledge from the massive corpora on which they are trained, and prompt engineering seeks to align these models to specific tasks.
Out of One, Many: Using Language Models to Simulate Human Samples
We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research.
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models
We explore the idea of compressing the prompts used to condition language models, and show that compressed prompts can retain a substantive amount of information about the original prompt.
AI Chat Assistants can Improve Conversations about Divisive Topics
A rapidly increasing amount of human conversation occurs online.
Towards Coding Social Science Datasets with Language Models
This provides exciting evidence that language models can serve as a critical advance in the coding of open-ended texts in a variety of applications.
