Evidence map›Paper›PMID 39694977›Full record

ArticleBehavior research methods2024

MacBehaviour: An R package for behavioural experimentation on large language models.

Xufeng Duan, Shixuan Li, Zhenguang G Cai

Abstract read
In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xufeng DuanDepartment of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong, China. xufeng.duan@link.cuhk.edu.hk.
Shixuan LiDepartment of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong, China.
Zhenguang G CaiDepartment of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong, China. zhenguangcai@cuhk.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of large language models (LLMs) and LLM-powered chatbots has gained significant attention in recent years, with researchers treating LLMs as participants in psychological experiments. To facilitate this research, we developed an R package called "MacBehaviour " ( https://github.com/xufengduan/MacBehaviour ), which interacts with over 100 LLMs, including OpenAI's GPT family, the Claude family, Gemini, Llama family, and other open-weight models. The package streamlines the processes of LLM behavioural experimentation by providing a comprehensive set of functions for experiment design, stimuli presentation, model behaviour manipulation, and logging responses and token probabilities. With a few lines of code, researchers can seamlessly set up and conduct psychological experiments, making LLM behaviour studies highly accessible. To validate the utility and effectiveness of "MacBehaviour," we conducted three experiments on GPT-3.5 Turbo, Llama-2-7b-chat-hf, and Vicuna-1.5-13b, replicating the sound-gender association in LLMs. The results consistently demonstrated that these LLMs exhibit human-like tendencies to infer gender from novel personal names based on their phonology, as previously shown by Cai et al. (2024). In conclusion, "MacBehaviour" is a user-friendly R package that simplifies and standardises the experimental process for machine behaviour studies, offering a valuable tool for researchers in this field.

Indexed as

Behavioral ResearchLanguageSoftwareHumansMaleExperimentationLarge language modelsMachine behaviourR package

Identifiers

PMID39694977
PMCPMC11655609

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.