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..
Memorizing Documents with Guidance in Large Language Models
Authors: Bumjin Park, Jaesik Choi
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Wikitext-103-v1 with Pythia1B show that the proposed methods provide different memory entries for documents and high recall of document-related content in generation with trained document-wise memories. |
| Researcher Affiliation | Academia | Bumjin Park 1 and Jaesik Choi1,2 1KAIST AI 2INEEJI EMAIL |
| Pseudocode | No | The paper does not include a dedicated section or figure for pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make the source code publicly available.2 https://github.com/fxnnxc/Doc Guidance LLM |
| Open Datasets | Yes | We train Pythia 1B [Biderman et al., 2023] to memorize Wikitext-103-v1 [Merity et al., 2017] |
| Dataset Splits | No | The paper mentions using Wikitext-103-v1 but does not explicitly provide details about specific train/validation/test dataset splits used for the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We individually train document-wise memories for 10, 20, and 50 documents with guidance α = 0.1 and τ = 2.5. For baselines, we train two types of memory modules without guidance. Shared is the MLP in Equation 3, and Add is a module that directly adds differential memory entries. We also evaluate three activation types for document memory entries: Re LU, Tanh, and Sigmoid, which affect memory selections. |