Noah D. Goodman

research

∙ 09/11/2023

Hypothesis Search: Inductive Reasoning with Language Models

Inductive reasoning is a core problem-solving capacity: humans can ident...

0 Ruocheng Wang, et al. ∙

research

∙ 06/22/2023

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

How does language inform our downstream thinking? In particular, how do ...

0 Lionel Wong, et al. ∙

research

∙ 06/21/2023

Understanding Social Reasoning in Language Models with Language Models

As Large Language Models (LLMs) become increasingly integrated into our ...

0 Kanishk Gandhi, et al. ∙

research

∙ 06/16/2023

Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness

Language model training in distributed settings is limited by the commun...

0 Eric Zelikman, et al. ∙

research

∙ 06/06/2023

Certified Reasoning with Language Models

Language models often achieve higher accuracy when reasoning step-by-ste...

0 Gabriel Poesia, et al. ∙

research

∙ 05/30/2023

Strategic Reasoning with Language Models

Strategic reasoning enables agents to cooperate, communicate, and compet...

0 Kanishk Gandhi, et al. ∙

research

∙ 05/19/2023

Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems

Humans teach others about the world through language and demonstration. ...

0 Dhara Yu, et al. ∙

research

∙ 05/15/2023

Interpretability at Scale: Identifying Causal Mechanisms in Alpaca

Obtaining human-interpretable explanations of large, general-purpose lan...

0 Zhengxuan Wu, et al. ∙

research

∙ 05/11/2023

Overinformative Question Answering by Humans and Machines

When faced with a polar question, speakers often provide overinformative...

0 Polina Tsvilodub, et al. ∙

research

∙ 05/05/2023

Bayesian Reinforcement Learning with Limited Cognitive Load

All biological and artificial agents must learn and make decisions given...

0 Dilip Arumugam, et al. ∙

research

∙ 04/16/2023

Solving Math Word Problems by Combining Language Models With Symbolic Solvers

Automatically generating high-quality step-by-step solutions to math wor...

0 Joy He-Yueya, et al. ∙

research

∙ 04/07/2023

Why think step-by-step? Reasoning emerges from the locality of experience

Humans have a powerful and mysterious capacity to reason. By working thr...

0 Ben Prystawski, et al. ∙

research

∙ 03/05/2023

Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations

Causal abstraction is a promising theoretical framework for explainable ...

0 Atticus Geiger, et al. ∙

research

∙ 12/20/2022

Parsel: A Unified Natural Language Framework for Algorithmic Reasoning

Despite recent success in large language model (LLM) reasoning, LLMs sti...

0 Eric Zelikman, et al. ∙

research

∙ 12/01/2022

Flexible social inference facilitates targeted social learning when rewards are not observable

Relying on others can be as risky as it can be rewarding. Advice seekers...

0 Robert D. Hawkins, et al. ∙

research

∙ 11/29/2022

Peano: Learning Formal Mathematical Reasoning

General mathematical reasoning is computationally undecidable, but human...

0 Gabriel Poesia, et al. ∙

research

∙ 10/30/2022

On Rate-Distortion Theory in Capacity-Limited Cognition Reinforcement Learning

Throughout the cognitive-science literature, there is widespread agreeme...

0 Dilip Arumugam, et al. ∙

research

∙ 05/18/2022

Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

Speakers' referential expressions often depart from communicative ideals...

0 Fei Fang, et al. ∙

research

∙ 03/28/2022

STaR: Bootstrapping Reasoning With Reasoning

Generating step-by-step "chain-of-thought" rationales improves language ...

1 Eric Zelikman, et al. ∙

research

∙ 12/05/2021

Causal Distillation for Language Models

Distillation efforts have led to language models that are more compact a...

0 Zhengxuan Wu, et al. ∙

research

∙ 12/01/2021

Inducing Causal Structure for Interpretable Neural Networks

In many areas, we have well-founded insights about causal structure that...

0 Atticus Geiger, et al. ∙

research

∙ 10/11/2021

Calibrate your listeners! Robust communication-based training for pragmatic speakers

To be good conversational partners, natural language processing (NLP) sy...

0 Rose E. Wang, et al. ∙

research

∙ 04/16/2021

Concadia: Tackling image accessibility with context

Images have become an integral part of online media. This has enhanced s...

0 Elisa Kreiss, et al. ∙

research

∙ 04/12/2021

From partners to populations: A hierarchical Bayesian account of coordination and convention

Languages are powerful solutions to coordination problems: they provide ...

10 Robert D. Hawkins, et al. ∙

research

∙ 05/31/2020

Learning to refer informatively by amortizing pragmatic reasoning

A hallmark of human language is the ability to effectively and efficient...

0 Julia White, et al. ∙

research

∙ 02/04/2020

Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks

A key property of linguistic conventions is that they hold over an entir...

0 Robert D. Hawkins, et al. ∙

research

∙ 12/16/2019

Characterizing the dynamics of learning in repeated reference games

The language we use over the course of conversation changes as we establ...

0 Robert D. Hawkins, et al. ∙

research

∙ 11/22/2019

Continual adaptation for efficient machine communication

To communicate with new partners in new contexts, humans rapidly form ne...

0 Robert D. Hawkins, et al. ∙

research

∙ 09/12/2019

Analyzing machine-learned representations: A natural language case study

As modern deep networks become more complex, and get closer to human-lik...

0 Ishita Dasgupta, et al. ∙

research

∙ 06/04/2019

Learning to Explain: Answering Why-Questions via Rephrasing

Providing plausible responses to why questions is a challenging but crit...

0 Allen Nie, et al. ∙

research

∙ 05/08/2019

ShapeGlot: Learning Language for Shape Differentiation

In this work we explore how fine-grained differences between the shapes ...

2 Panos Achlioptas, et al. ∙

research

∙ 03/19/2019

When redundancy is rational: A Bayesian approach to 'overinformative' referring expressions

Referring is one of the most basic and prevalent uses of language. How d...

0 Judith Degen, et al. ∙

research

∙ 03/15/2019

Applying Probabilistic Programming to Affective Computing

Affective Computing is a rapidly growing field spurred by advancements i...

0 Desmond C. Ong, et al. ∙

research

∙ 11/21/2018

Joint Mapping and Calibration via Differentiable Sensor Fusion

We leverage automatic differentiation (AD) and probabilistic programming...

0 Jonathan P. Chen, et al. ∙

research

∙ 10/18/2018

Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platfo...

0 Eli Bingham, et al. ∙

research

∙ 09/30/2018

An Incremental Iterated Response Model of Pragmatics

Recent Iterated Response (IR) models of pragmatics conceptualize languag...

0 Reuben Cohn-Gordon, et al. ∙

research

∙ 07/24/2018

Speakers account for asymmetries in visual perspective so listeners don't have to

Debates over adults' theory of mind use have been fueled by surprising f...

0 Robert X. D. Hawkins, et al. ∙

research

∙ 05/30/2018

Planning, Inference and Pragmatics in Sequential Language Games

We study sequential language games in which two players, each with priva...

0 Fereshte Khani, et al. ∙

research

∙ 02/12/2018

Evaluating Compositionality in Sentence Embeddings

An important frontier in the quest for human-like AI is compositional se...

0 Ishita Dasgupta, et al. ∙

research

∙ 10/12/2017

DisSent: Sentence Representation Learning from Explicit Discourse Relations

Sentence vectors represent an appealing approach to meaning: learn an em...

0 Allen Nie, et al. ∙

research

∙ 06/01/2017

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...

0 N. Siddharth, et al. ∙

research

∙ 03/29/2017

Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding

We present a model of pragmatic referring expression interpretation in a...

0 Will Monroe, et al. ∙

research

∙ 11/22/2016

Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...

0 N. Siddharth, et al. ∙

research

∙ 10/18/2016

Deep Amortized Inference for Probabilistic Programs

Probabilistic programming languages (PPLs) are a powerful modeling tool,...

0 Daniel Ritchie, et al. ∙

research

∙ 08/09/2016

A pragmatic theory of generic language

Generalizations about categories are central to human understanding, and...

0 Michael Henry Tessler, et al. ∙

research

∙ 06/13/2016

Learning to Generate Compositional Color Descriptions

The production of color language is essential for grounded language gene...

0 Will Monroe, et al. ∙

research

∙ 03/19/2016

Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) ...

0 Daniel Ritchie, et al. ∙

research

∙ 12/18/2015

Learning the Preferences of Ignorant, Inconsistent Agents

An important use of machine learning is to learn what people value. What...

0 Owain Evans, et al. ∙

research

∙ 09/09/2015

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs

Many practical techniques for probabilistic inference require a sequence...

0 Andreas Stuhlmüller, et al. ∙

research

∙ 09/07/2015

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Lightweight, source-to-source transformation approaches to implementing ...

0 Daniel Ritchie, et al. ∙

Noah D. Goodman

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