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

arXiv 2405.07495 ↗First posted 13 May 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

There has been increasing interest in investigating the behaviours of large language models (LLMs) and LLM-powered chatbots by treating an LLM as a participant in a psychological experiment. We therefore developed an R package called "MacBehaviour" that aims to interact with more than 60 language models in one package (e.g., OpenAI's GPT family, the Claude family, Gemini, Llama family, and open-source models) and streamline the experimental process of LLMs behaviour experiments. The package offers a comprehensive set of functions designed for LLM experiments, covering experiment design, stimuli presentation, model behaviour manipulation, logging response and token probability. To demonstrate the utility and effectiveness of "MacBehaviour," we conducted three validation experiments on three LLMs (GPT-3.5, Llama-2 7B, and Vicuna-1.5 13B) to replicate sound-gender association in LLMs. The results consistently showed that they exhibit human-like tendencies to infer gender from novel personal names based on their phonology, as previously demonstrated (Cai et al., 2023). In summary, "MacBehaviour" is an R package for machine behaviour studies which offers a user-friendly interface and comprehensive features to simplify and standardize the experimental process.

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References · 22

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  3. 2022Emergent Abilities of Large Language Modelscited by 2,483
  4. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  5. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  6. 2022Using cognitive psychology to understand GPT-3cited by 389
  7. 2022Reasoning with Language Model Prompting: A Surveycited by 251
  8. 2022Emergent Analogical Reasoning in Large Language Modelscited by 239
  9. 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
  10. 2022Language models show human-like content effects on reasoning taskscited by 171
  11. 2023Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificationcited by 166
  12. 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 159
10 more
  1. 2022Who is GPT-3? An Exploration of Personality, Values and Demographicscited by 112
  2. 2024HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalizationcited by 84
  3. 2016Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learnercited by 68
  4. 2022Do Large Language Models know what humans know?cited by 60
  5. 2022Estimating the Personality of White-Box Language Modelscited by 47
  6. 2023Prompting is not a substitute for probability measurements in large language modelscited by 43
  7. 2023A Latent Space Theory for Emergent Abilities in Large Language Modelscited by 42
  8. 2023Do large language models resemble humans in language use?cited by 34
  9. 2023Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Modelscited by 16
  10. 2024Towards a Psychology of Machines: Large Language Models Predict Human Memorycited by 4

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