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..
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture
Authors: Sangjun Park, Jinyeong Bak
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results prove the effectiveness of Memoria in the diverse tasks of sorting, language modeling, and classification, surpassing conventional techniques. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea. Correspondence to: Jin Yeong Bak <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Retrieve Stage |
| Open Source Code | Yes | 1The implementation of Memoria and all experimental code are publicly available at https://github.com/cosmoquester/memoria |
| Open Datasets | Yes | Secondly, we performed language modeling for token-level on WikiText-103 (Raw) (Merity et al., 2017) and PG-19 (Rae et al., 2020), and character-level on enwik8 (Mahoney, 2006). ... Lastly, we conducted the classification task on the long document classification dataset, Hyperpartisan (Kiesel et al., 2019). |
| Dataset Splits | Yes | We report validation and test set results because of data distribution discrepancies. |
| Hardware Specification | Yes | One or more NVIDIA A100 or A6000 GPUs were used for training. |
| Software Dependencies | No | The paper mentions software like GPT-2 tokenizer, Adam optimizer, linear scheduler, and PyTorch, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For all sorting experiments, a batch size of 32, a warmup rate of 0.06, a learning rate of 2e-4, and an epoch of 5 were used for 80,000 train examples. Memoria parameters used in the experiment were as follows: an initial lifespan of 5, a lifespan extension scale α of 8, and a long-term memory search depth Ndepth of 10 in all cases. |