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..
Knowledge Circuits in Pretrained Transformers
Authors: Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments, conducted with GPT2 and Tiny LLAMA, have allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. |
| Researcher Affiliation | Collaboration | 1 Zhejiang University 2 National University of Singapore, NUS-NCS Joint Lab, Singapore 3 Zhejiang Key Laboratory of Big Data Intelligent Computing |
| Pseudocode | No | The paper describes methods and processes in textual form and through mathematical equations but does not include any dedicated pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | Code and data are available in https://github.com/zjunlp/Knowledge Circuits. |
| Open Datasets | Yes | We utilize the dataset provided by LRE [42] and consider different kinds of knowledge, including linguistic, commonsense, fact, and bias. |
| Dataset Splits | Yes | To evaluate completeness, we first construct the circuit using the validation data Dval for a specific knowledge type and then test its performance on the test split Dtest in isolation. |
| Hardware Specification | Yes | We use the NVIDIA-A800 (40GB) to conduct our experiments. |
| Software Dependencies | No | The paper mentions using specific toolkits like "Automated Circuit Discovery [32] toolkit" and "transformer lens [41]", and frameworks like "Easy Edit[74]", but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The primary hyperparameter for constructing a circuit is the threshold τ used to detect performance drops... In our experiment, we test τ values from the set {0.02, 0.01, 0.005} to determine the appropriate circuit size for different types of knowledge. |