How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
Authors: Yuchen Li, Yuanzhi Li, Andrej Risteski
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Precisely, we show, through a combination of mathematical analysis and experiments on Wikipedia data and synthetic data modeled by Latent Dirichlet Allocation (LDA), that the embedding layer and the self-attention layer encode the topical structure. We analyze properties of the training dynamics via extensive experimental analysis. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2Microsoft Research. Correspondence to: Yuchen Li <yuchenl4@cs.cmu.edu>. |
| Pseudocode | No | The paper describes mathematical analyses and experimental procedures but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is released at https://github.com/ Yuchen Li01/transformer_topic_model_LDA |
| Open Datasets | Yes | We focus on understanding the optimization dynamics of transformers in a simple sandbox: a single-layer transformer trained on (synthetic) data following a topic model distribution and validate that our results robustly transfer to real data (Wikipedia Wikimedia Foundation, 2023). In our synthetic data experiments, we use a finite N and generate data using an LDA model (Blei et al., 2003) |
| Dataset Splits | No | The paper mentions using 'synthetic data' and 'Wikipedia data' for experiments but does not explicitly provide details about train/validation/test splits (e.g., specific percentages, sample counts, or references to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not mention any specific hardware components (e.g., GPU models, CPU types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Huggingface (Wolf et al., 2020)' for pre-trained models and tokenizers, and refers to 'standard implementation in Wolf et al. (2020)'. However, it does not provide specific version numbers for any software libraries or dependencies (e.g., PyTorch version, Transformers library version). |
| Experiment Setup | Yes | In our experiments, we generate data following Section 3.1 with T = 10, v = 10, N uniformly randomly chosen from [100, 150]... Our training objective follows Section 3.2 with pm = 0.15, pc = 0.1, pr = 0.1 following Devlin et al. (2019). We use the model architecture following Section 3.3 but add back the bias terms b K, b Q, b V , following standard implementation in Wolf et al. (2020). |