134 citations · 242 across the 39 of their papers we have counts for
17 papers · 1 filter
Quantifying Knowledge Distillation Using Partial Information Decomposition
Pasan Dissanayake, Faisal Hamman, Barproda Halder +3
Knowledge distillation deploys complex machine learning models in resource-constrained environments by training a smaller student model to emulate internal representations of a com…
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Ryan Liu, Jiayi Geng, Addison J. Wu +3
Chain-of-thought (CoT) prompting has become a widely used strategy for improving large language and multimodal model performance. However, it is still an open question under which…
Adaptive Language-Guided Abstraction from Contrastive Explanations
Andi Peng, Belinda Z. Li, Ilia Sucholutsky +4
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of th…
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
Zhonghao He, Jascha Achterberg, Katie Collins +13
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this pro…
Building Machines that Learn and Think with People
Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt +10
What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and t…
Modulating Language Model Experiences through Frictions
Katherine M. Collins, Valerie Chen, Ilia Sucholutsky +6
Language models are transforming the ways that their users engage with the world. Despite impressive capabilities, over-consumption of language model outputs risks propagating unch…
Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition
Barproda Halder, Faisal Hamman, Pasan Dissanayake +3
Spuriousness arises when there is an association between two or more variables in a dataset that are not causally related. In this work, we propose an explainability framework to p…
Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People
Dun-Ming Huang, Pol Van Rijn, Ilia Sucholutsky +2
Conversational tones -- the manners and attitudes in which speakers communicate -- are essential to effective communication. Amidst the increasing popularization of Large Language…
Large Language Models Assume People are More Rational than We Really are
Ryan Liu, Jiayi Geng, Joshua C. Peterson +2
In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit int…
Representational Alignment Supports Effective Machine Teaching
Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya +11
A good teacher should not only be knowledgeable, but should also be able to communicate in a way that the student understands -- to share the student's representation of the world.…
Learning with Language-Guided State Abstractions
Andi Peng, Ilia Sucholutsky, Belinda Z. Li +4
We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is faci…
Analyzing the Roles of Language and Vision in Learning from Limited Data
Allison Chen, Ilia Sucholutsky, Olga Russakovsky +1
Does language help make sense of the visual world? How important is it to actually see the world rather than having it described with words? These basic questions about the nature…
Large language models surpass human experts in predicting neuroscience results
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun +36
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offe…
A Rational Analysis of the Speech-to-Song Illusion
Raja Marjieh, Pol van Rijn, Ilia Sucholutsky +3
The speech-to-song illusion is a robust psychological phenomenon whereby a spoken sentence sounds increasingly more musical as it is repeated. Despite decades of research, a comple…
Preference-Conditioned Language-Guided Abstraction
Andi Peng, Andreea Bobu, Belinda Z. Li +5
Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations. Recent work constructs state abstractions, i.e. visual rep…
Measuring Implicit Bias in Explicitly Unbiased Large Language Models
Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky +1
Large language models (LLMs) can pass explicit social bias tests but still harbor implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit subtle biases. Meas…
Concept Alignment
Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky +2
Discussion of AI alignment (alignment between humans and AI systems) has focused on value alignment, broadly referring to creating AI systems that share human values. We argue that…