Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
Authors: Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang, Danica J. Sutherland
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper outlines key characteristics of agents behavior in the Bayesian-IL framework, including predictions that are supported by experimental verification with various LLMs. |
| Researcher Affiliation | Academia | Yi Ren UBC EMAIL Shangmin Guo University of Edinburgh EMAIL Linlu Qiu MIT EMAIL Bailin Wang MIT EMAIL Danica J. Sutherland UBC & Amii EMAIL |
| Pseudocode | No | The paper describes algorithms and procedures conceptually but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | The code for experiments is available at https://github.com/Joshua-Ren/i ICL. |
| Open Datasets | Yes | We finetune a pretrained llama-2-7B model (Touvron et al. 2023) using Antropic-HH dataset (Bai et al. 2022) |
| Dataset Splits | No | The paper describes initial data (`d0`) and data pools (`Dpool`) used in experiments, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined split citations) needed for reproducibility. |
| Hardware Specification | No | The paper mentions the use of LLMs (GPT3.5, GPT4, Claude3-haiku, Mixtral-8x7b) for experiments, but it does not specify the underlying hardware (e.g., GPU/CPU models, memory, or specific cloud instances) used for running these experiments. |
| Software Dependencies | No | The paper mentions using specific LLMs (GPT3.5, GPT4, Claude3-haiku, Mixtral-8x7b) but does not provide details on specific software dependencies, libraries, or frameworks with version numbers (e.g., Python, PyTorch, TensorFlow) for reproducibility. |
| Experiment Setup | Yes | The temperature is 0.1 and the probability feedback is enabled. |