arXiv subjects

Gabriel Simmons

Publications and source records attributed to Gabriel Simmons.

5 recordsLinked to original sources

arXiv2025

Heads or Tails: A Simple Example of Causal Abstractive Simulation

This note illustrates how a variety of causal abstraction arXiv:1707.00819 arXiv:1812.03789, defined here as causal abstractive simulation, can be used to formalize a simple example of language model simulation. This note considers the case of simulating a fair coin toss with a language model. Examples are presented illustrating the ways language models can fail to simulate, and a success case is presented, illustrating how this formalism may be used to prove that a language model simulates some other system, given a causal description of the system. This note may be of interest to three groups. For practitioners in the growing field of language model simulation, causal abstractive simulation is a means to connect ad-hoc statistical benchmarking practices to the solid formal foundation of causality. Philosophers of AI and philosophers of mind may be interested as causal abstractive simulation gives a precise operationalization to the idea that language models are role-playing arXiv:2402.12422. Mathematicians and others working on causal abstraction may be interested to see a new application of the core ideas that yields a new variation of causal abstraction.

cs.AI↗

arXiv2024

Comment on Is Complexity an Illusion?

The paper "Is Complexity an Illusion?" (Bennett, 2024) provides a formalism for complexity, learning, inference, and generalization, and introduces a formal definition for a "policy". This reply shows that correct policies do not exist for a simple task of supervised multi-class classification, via mathematical proof and exhaustive search. Implications of this result are discussed, as well as possible responses and amendments to the theory.

cs.AI↗

arXiv2024

Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning

This study evaluates the ability of Large Language Model (LLM)-based Subpopulation Representative Models (SRMs) to generalize from empirical data, utilizing in-context learning with data from the 2016 and 2020 American National Election Studies. We explore generalization across response variables and demographic subgroups. While conditioning with empirical data improves performance on the whole, the benefit of in-context learning varies considerably across demographics, sometimes hurting performance for one demographic while helping performance for others. The inequitable benefits of in-context learning for SRM present a challenge for practitioners implementing SRMs, and for decision-makers who might come to rely on them. Our work highlights a need for fine-grained benchmarks captured from diverse subpopulations that test not only fidelity but generalization.

cs.LG↗

arXiv2023

Large Language Models as Subpopulation Representative Models: A Review

Of the many commercial and scientific opportunities provided by large language models (LLMs; including Open AI's ChatGPT, Meta's LLaMA, and Anthropic's Claude), one of the more intriguing applications has been the simulation of human behavior and opinion. LLMs have been used to generate human simulcra to serve as experimental participants, survey respondents, or other independent agents, with outcomes that often closely parallel the observed behavior of their genuine human counterparts. Here, we specifically consider the feasibility of using LLMs to estimate subpopulation representative models (SRMs). SRMs could provide an alternate or complementary way to measure public opinion among demographic, geographic, or political segments of the population. However, the introduction of new technology to the socio-technical infrastructure does not come without risk. We provide an overview of behavior elicitation techniques for LLMs, and a survey of existing SRM implementations. We offer frameworks for the analysis, development, and practical implementation of LLMs as SRMs, consider potential risks, and suggest directions for future work.

cs.CY↗

arXiv2023

Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity

Large Language Models (LLMs) have demonstrated impressive capabilities in generating fluent text, as well as tendencies to reproduce undesirable social biases. This study investigates whether LLMs reproduce the moral biases associated with political groups in the United States, an instance of a broader capability herein termed moral mimicry. This hypothesis is explored in the GPT-3/3.5 and OPT families of Transformer-based LLMs. Using tools from Moral Foundations Theory, it is shown that these LLMs are indeed moral mimics. When prompted with a liberal or conservative political identity, the models generate text reflecting corresponding moral biases. This study also explores the relationship between moral mimicry and model size, and similarity between human and LLM moral word use.

cs.CL↗