112 citations · 169 across the 14 of their papers we have counts for
17 papers
Can AI mediation improve democratic deliberation?
Michael Henry Tessler, Georgina Evans, Michiel A. Bakker +8
The strength of democracy lies in the free and equal exchange of diverse viewpoints. Living up to this ideal at scale faces inherent tensions: broad participation, meaningful delib…
Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value
Joe Edelman, Tan Zhi-Xuan, Ryan Lowe +30
Beneficial societal outcomes cannot be guaranteed by aligning individual AI systems with the intentions of their operators or users. Even an AI system that is perfectly aligned to…
DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents
Yun-Shiuan Chuang, Ruixuan Tu, Chengtao Dai +9
Modeling opinion change through social interactions is important for understanding polarization, misinformation, and societal conflict. Recent work simulates opinion dynamics with…
Language and Experience: A Computational Model of Social Learning in Complex Tasks
Cédric Colas, Tracey Mills, Ben Prystawski +4
The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people i…
Value Profiles for Encoding Human Variation
Taylor Sorensen, Pushkar Mishra, Roma Patel +6
Modelling human variation in rating tasks is crucial for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using n…
Language Agents as Digital Representatives in Collective Decision-Making
Daniel Jarrett, Miruna Pîslar, Michiel A. Bakker +6
Consider the process of collective decision-making, in which a group of individuals interactively select a preferred outcome from among a universe of alternatives. In this context,…
AI and the Future of Digital Public Squares
Beth Goldberg, Diana Acosta-Navas, Michiel Bakker +24
Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large lan…
A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI
Seliem El-Sayed, Canfer Akbulut, Amanda McCroskery +17
Recent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generat…
Fine-tuning language models to find agreement among humans with diverse preferences
Michiel A. Bakker, Martin J. Chadwick, Hannah R. Sheahan +8
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static…
Assessing Group-level Gender Bias in Professional Evaluations: The Case of Medical Student End-of-Shift Feedback
Emmy Liu, Michael Henry Tessler, Nicole Dubosh +2
Although approximately 50% of medical school graduates today are women, female physicians tend to be underrepresented in senior positions, make less money than their male counterpa…
Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan +6
Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We the…
Learning to solve complex tasks by growing knowledge culturally across generations
Michael Henry Tessler, Jason Madeano, Pedro A. Tsividis +3
Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn res…
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum +1
Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2"). Neural sequen…
Communicating Natural Programs to Humans and Machines
Samuel Acquaviva, Yewen Pu, Marta Kryven +6
The Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, th…
A practical introduction to the Rational Speech Act modeling framework
Gregory Scontras, Michael Henry Tessler, Michael Franke
Recent advances in computational cognitive science (i.e., simulation-based probabilistic programs) have paved the way for significant progress in formal, implementable models of pr…
Practical optimal experiment design with probabilistic programs
Long Ouyang, Michael Henry Tessler, Daniel Ly +1
Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could ru…
The Language of Generalization
Michael Henry Tessler, Noah D. Goodman
Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly", "John hikes", "Fire makes smoke"). Though found in every language and emerg…