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ProgressGym-HistLlama3-8B-C014-instruct-v0.2

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metadata
license: cc-by-4.0
tags:
  - alignment
  - value alignment
  - AI safety
  - safety
  - LLM
  - history
datasets:
  - PKU-Alignment/ProgressGym-HistText
  - PKU-Alignment/ProgressGym-TimelessQA
base_model:
  - PKU-Alignment/ProgressGym-HistLlama3-8B-C014-pretrain
  - meta-llama/Meta-Llama-3-8B

ProgressGym-HistLlama3-8B-C014-instruct

Overview

The ProgressGym Framework

Framework Diagram

ProgressGym-HistLlama3-8B-C014-instruct is part of the ProgressGym framework for research and experimentation on progress alignment - the emulation of moral progress in AI alignment algorithms, as a measure to prevent risks of societal value lock-in.

To quote the paper ProgressGym: Alignment with a Millennium of Moral Progress:

Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale.

We introduce progress alignment as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots.

ProgressGym-HistLlama3-8B-C014-instruct

ProgressGym-HistLlama3-8B-C014-instruct is one of the 36 historical language models in the ProgressGym framework.

ProgressGym-HistLlama3-8B-C014-instruct is under continual iteration. Improving upon the current version, new versions of the model are currently being trained to reflect historical moral tendencies in ever more comprehensive ways.

ProgressGym-HistLlama3-8B-C014-instruct is a 14th-century historical language model. Based on Meta-Llama-3-8B, It is continued-pretrained on the 14th-century text data from ProgressGym-HistText, using the following hyperparameters:

  • learning_rate: 1.5e-05
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: polynomial
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 4.0
  • mixed_precision_training: Native AMP

... with the following training results:

Training Loss Epoch Step Validation Loss
2.5789 0.0152 1 2.6458
2.5672 0.0758 5 2.6280
2.5751 0.1515 10 2.5314
2.418 0.2273 15 2.4634
2.4701 0.3030 20 2.4177
2.3904 0.3788 25 2.3785
2.3539 0.4545 30 2.3378
2.3101 0.5303 35 2.3082
2.3254 0.6061 40 2.2816
2.2762 0.6818 45 2.2614
2.2525 0.7576 50 2.2458
2.2777 0.8333 55 2.2321
2.2054 0.9091 60 2.2206
2.237 0.9848 65 2.2113
1.986 1.0606 70 2.2115
1.9373 1.1364 75 2.2217
1.9228 1.2121 80 2.2132
1.9084 1.2879 85 2.2118
1.9684 1.3636 90 2.2122
1.9126 1.4394 95 2.2094
1.9101 1.5152 100 2.2066
1.8496 1.5909 105 2.2058
1.9154 1.6667 110 2.2057
1.9233 1.7424 115 2.2056
1.9198 1.8182 120 2.2052
1.9229 1.8939 125 2.2048
1.8913 1.9697 130 2.2045
1.8814 2.0455 135 2.2046
1.8813 2.1212 140 2.2051
1.8912 2.1970 145 2.2058
1.9184 2.2727 150 2.2065
1.8662 2.3485 155 2.2071
1.8809 2.4242 160 2.2074
1.8591 2.5 165 2.2077
1.8731 2.5758 170 2.2079
1.8948 2.6515 175 2.2082
1.8876 2.7273 180 2.2082
1.8408 2.8030 185 2.2083
1.8931 2.8788 190 2.2082
1.8569 2.9545 195 2.2080
1.8621 3.0303 200 2.2079
1.8863 3.1061 205 2.2078
1.9021 3.1818 210 2.2079
1.8648 3.2576 215 2.2080
1.8443 3.3333 220 2.2081
1.8978 3.4091 225 2.2080
1.8658 3.4848 230 2.2080
1.8706 3.5606 235 2.2079
1.8855 3.6364 240 2.2078
1.8535 3.7121 245 2.2078
1.9062 3.7879 250 2.2079
1.8628 3.8636 255 2.2078
1.8484 3.9394 260 2.2077

Note that the training data volume for the continued pretraining stage is capped at 3GB. When the corresponding century's corpus exceeds this volume, the training data is randomly sampled to fit the volume.

ProgressGym-HistLlama3-8B-C014-instruct is an instruction-tuned language model. It is tuned on ProgressGym-TimelessQA, using the following hyperparameters. Note, however, that the snapshot at training step 10 is used for the final model, to minimize erosion of the value tendencies learned during continued pretraining; we qualitatively observe that this snapshot still possesses strong instruction-following capabilities.

  • learning_rate: 1.5e-05
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: polynomial
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 4.0
  • mixed_precision_training: Native AMP

... with the following training results:

Training Loss Epoch Step Validation Loss
0.9832 0.0208 1 0.9730
0.9463 0.1042 5 0.9421
0.8488 0.2083 10 0.8247
0.7833 0.3125 15 0.8149
0.7797 0.4167 20 0.8403
0.8542 0.5208 25 0.8670
0.8895 0.625 30 0.8718
0.8519 0.7292 35 0.8592
0.8224 0.8333 40 0.8491
0.8538 0.9375 45 0.8384
0.6569 1.0417 50 0.8295
0.437 1.1458 55 0.8457
0.4405 1.25 60 0.8668
0.4331 1.3542 65 0.8671
0.448 1.4583 70 0.8597
0.4673 1.5625 75 0.8514
0.4298 1.6667 80 0.8474
0.4252 1.7708 85 0.8458
0.4429 1.875 90 0.8451
0.4484 1.9792 95 0.8450
0.3634 2.0833 100 0.8455
0.3876 2.1875 105 0.8467
0.3717 2.2917 110 0.8481
0.387 2.3958 115 0.8494
0.3561 2.5 120 0.8505
0.4219 2.6042 125 0.8516
0.3798 2.7083 130 0.8527
0.3551 2.8125 135 0.8537
0.3827 2.9167 140 0.8546
0.3938 3.0208 145 0.8556
0.3805 3.125 150 0.8565
0.3813 3.2292 155 0.8574
0.3894 3.3333 160 0.8582
0.3603 3.4375 165 0.8589
0.3515 3.5417 170 0.8597
0.3433 3.6458 175 0.8605
0.3511 3.75 180 0.8614
0.3599 3.8542 185 0.8620
0.3994 3.9583 190 0.8621

Links

Citation

If the datasets, models, or framework of ProgressGym help you in your project, please cite ProgressGym using the bibtex entry below.

@article{progressgym,
  title={ProgressGym: Alignment with a Millennium of Moral Progress},
  author={Tianyi Qiu and Yang Zhang and Xuchuan Huang and Jasmine Xinze Li and Jiaming Ji and Yaodong Yang},
  journal={arXiv preprint arXiv:2406.20087},
  eprint={2406.20087},
  eprinttype = {arXiv},
  year={2024}
}

Ethics Statement

  • Copyright information of historical text data sources:
    • Project Gutenberg, one among our four source of our historical text data, consists only of texts in the public domain.
    • For the text that we draw from Internet Archive, we only include those that uploaded by Library of Congress, which are texts freely released online by the U.S. Library of Congress for research and public use.
    • The text data from Early English Books Online are, according to their publisher, "freely available to the public" and "available for access, distribution, use, or reuse by anyone".
    • The last remaining source of our historical text data, the Pile of Law dataset, is released under a Creative Commons license, which we adhere to in our use.
  • Reproducibility: To ensure reproducibility, we open-source all the code involved in the production of our main results (including the entire pipeline starting from data collection and model training), as well as the supporting infrastructure (the ProgressGym framework), making replication as easy as running a few simple script files.
  • Misuse Prevention: In order to prevent potential misuse of progress alignment algorithms, we have carefully formulated progress alignment as strictly value-neutral, without a priori assumptions on the direction of progress. In the event of potential misuse of our dataset, we condemn any misuse attempt to the strongest degree possible, and will work with the research community on whistleblowing for such attempts.
  • Open-Sourcing: We confirm that our code, data, and models are to be open-sourced under a CC-BY 4.0 license. We will continue to maintain and update our open-source repositories and models.