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Found 30 papers, 8 papers with code
Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-The-Fly
Drawing real world social inferences usually requires taking into account information from multiple modalities.
What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
People regularly make inferences about objects in the world that they cannot see by flexibly integrating information from multiple sources: auditory and visual cues, language, and our prior beliefs and knowledge about the scene.
Using LLMs to Advance the Cognitive Science of Collectives
LLMs are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored.
Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships
Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks.
When Should We Orchestrate Multiple Agents?
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration.
General Scales Unlock AI Evaluation with Explanatory and Predictive Power
In this paper, we introduce general scales for AI evaluation that can explain what common AI benchmarks really measure, extract ability profiles of AI systems, and predict their performance for new task instances, in- and out-of-distribution.
Revisiting Rogers' Paradox in the Context of Human-AI Interaction
We consider strategies that can be undertaken by various stakeholders involved in a single human-AI interaction: human, AI model builder, and society or regulators around the interaction.
Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
The suite of datasets commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several shortcomings.
Can Large Language Models Understand Symbolic Graphics Programs?
While LLMs exhibit impressive skills in general program synthesis and analysis, symbolic graphics programs offer a new layer of evaluation: they allow us to test an LLM's ability to answer different-grained semantic-level questions of the images or 3D geometries without a vision encoder.
People use fast, goal-directed simulation to reason about novel games
People can evaluate features of problems and their potential solutions well before we can effectively solve them.
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation
Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established.
Modulating Language Model Experiences through Frictions
Through a user study with real humans, we observe shifts in user behavior from the imposition of a friction over LLMs in the context of a multi-topic question-answering task as a representative task that people may use LLMs for, e. g., in education and information retrieval.
Representational Alignment Supports Effective Machine Teaching
A good teacher should not only be knowledgeable; but should be able to communicate in a way that the student understands -- to share the student's representation of the world.
WatChat: Explaining perplexing programs by debugging mental models
Often, a good explanation for a program's unexpected behavior is a bug in the programmer's code.
Estimation of Concept Explanations Should be Uncertainty Aware
We believe the improved quality of uncertainty-aware concept explanations make them a strong candidate for more reliable model interpretation.
AI for Mathematics: A Cognitive Science Perspective
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Getting aligned on representational alignment
These questions pertaining to the study of representational alignment are at the heart of some of the most promising research areas in contemporary cognitive science, neuroscience, and machine learning.
Learning to Receive Help: Intervention-Aware Concept Embedding Models
To address this, we propose Intervention-aware Concept Embedding models (IntCEMs), a novel CBM-based architecture and training paradigm that improves a model's receptiveness to test-time interventions.
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
We propose FeedbackLogs, addenda to existing documentation of ML pipelines, to track the input of multiple stakeholders.
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
To test our model, we design and run a human experiment on a linguistic goal inference task.
Selective Concept Models: Permitting Stakeholder Customisation at Test-Time
Concept-based models perform prediction using a set of concepts that are interpretable to stakeholders.
Evaluating Language Models for Mathematics through Interactions
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants.
Learning Personalized Decision Support Policies
$\texttt{Modiste}$ leverages stochastic contextual bandit techniques to personalize a decision support policy for each decision-maker and supports extensions to the multi-objective setting to account for auxiliary objectives like the cost of support.
Human Uncertainty in Concept-Based AI Systems
We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans.
On the Informativeness of Supervision Signals
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans.
Human-in-the-Loop Mixup
We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration.
Eliciting and Learning with Soft Labels from Every Annotator
Our elicitation methodology therefore shows nuanced promise in enabling practitioners to enjoy the benefits of improved model performance and reliability with fewer annotators, and serves as a guide for future dataset curators on the benefits of leveraging richer information, such as categorical uncertainty, from individual annotators.
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs).
Learning Signal-Agnostic Manifolds of Neural Fields
We leverage neural fields to capture the underlying structure in image, shape, audio and cross-modal audiovisual domains in a modality-independent manner.
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.
