Luke Hewitt

I work on computational & experimental tools for measuring what changes people’s beliefs/attitudes, with application to effective advocacy, public health communication, AI safety, and social science methodology. My research combines RCTs, LLMs, expert forecasting and hierarchical Bayesian models.

Currently:

• I’m co-founder/director of Rhetorical Labs, a research collective which uses RCT experiments and machine learning to help public communication campaigns improve the impact of their messaging.
• I’m a Senior Research Fellow at Stanford PACS, where I study the capacity of Large Language Models to predict treatment effects in the social/behavioral sciences.
• I’m a member of the South Park Commons technical community (SF Bay hub)
• I’m co-PI for the SSRC Mercury Project team on Combatting health misinformation with community-crafted messaging.

Previously:

• AI safety consulting, OpenAI (GPT-4o persuasion evaluation)
• Research data scientist, Swayable (persuasion measurement, expt. design & analysis)
• PhD in AI / Cognitive Science, MIT
• MEng in Mathematical Computation, UCL

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Research

→ Quantifying the returns to persuasive message-targeting using a large archive of campaigns’ own experiments* - Tappin, Hewitt, Coppock (APSA 2024)

→ How will advanced AI systems impact democracy? Summerfield et al. (in review)

→ Leveraging Large Language Models to Predict Results of Experiments in the Social Sciences Hewitt*, Ashokkumar* et al. (in review)

→ GPT-4o System Card: Persuasion OpenAI (2024)

→ How experiments help campaigns persuade voters: evidence from a large archive of campaigns’ own experiments Hewitt et al. (APSR, 2024)

→ Using survey experiment pre-testing to support future pandemic response Tappin and Hewitt (PNAS Nexus, 2024)

→ Listening with generative models Cusimano et al. (Cognition, 2024)

→ Quantifying the persuasive returns to political microtargeting Tappin et al. (PNAS, 2023)

→ Emotion prediction as computation over a generative Theory of Mind Houlihan et al. (Phil. Trans. A, 2023)

→ DreamCoder: growing generalizable, interpretable knowledge with wake-sleep bayesian program learning Ellis et al. (Phil. Trans. A, 2023)

→ Rank-heterogeneous effects of political messages: Evidence from randomized survey experiments testing 59 video treatments Hewitt et al. (working paper)

→ Hybrid memoised wake-sleep: Approximate inference at the discrete-continuous interface Le et al. (ICLR, 2022)

→ DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning Ellis et al. (PLDI, 2021)

→ Estimating the Persistence of Party Cue Influence in a Panel Survey Experiment Tappin et al. (JEPS, 2021)

→ Learning to learn generative programs with memoised wake-sleep Hewitt et al. (UAI, 2020)

→ Inferring structured visual concepts from minimal data Qian et al. (CogSci, 2019)

→ Learning to infer program sketches Nye et al. (ICML, 2019)

→ The Variational Homoencoder: Learning to learn high capacity generative models from few examples Hewitt et al. (UAI, 2018)

→ Auditory scene analysis as Bayesian inference in sound source models Cusimano et al. (CogSci, 2017)