Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation
Authors: Randall Balestriero, Romain Cosentino, Sarath Shekkizhar
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results demonstrate how, even in large-scale regimes, exact theoretical results can answer practical questions in LLMs. Code: https://github.com/ Randall Balestriero/Spline LLM |
| Researcher Affiliation | Collaboration | 1Brown University, Computer Science Department 2Tenyx. Correspondence to: Randall Balestriero <rbalestr@brown.edu>, Romain Cosentino <romain@tenyx.com>, Sarath Shekkizhar <sarath@tenyx.com>. |
| Pseudocode | Yes | Listing 1. Code to use with the Llama Attention class in the modelling llama.py file of the Transformers package to obtain intrinsic dimension IDℓ ϵ(i) from Section 3.3; Listing 2. Code to use with the Llama MLP class in the modelling llama.py file of the Transformers package to obtain Eqs. (feature 1) to (feature 7). |
| Open Source Code | Yes | Code: https://github.com/ Randall Balestriero/Spline LLM |
| Open Datasets | Yes | Omni-Toxic Datasets: We use for the non-toxic samples: the concatenation of the subsampled (20, 000 samples) Pile validation dataset, with the questions from the Dolly Q&A datasets, as well as the non-toxic samples from the Jigsaw dataset (Adams et al., 2017). For the toxic samples: we use the toxic samples from the Jigaw dataset, concatenated with our hand-crafted toxic-pile dataset... Toxigen dataset (Hartvigsen et al., 2022). |
| Dataset Splits | No | The training procedure consists of using 70% of the dataset as the training set and evaluating the performance on the held-out 30% of the data. No explicit separate validation split is mentioned. |
| Hardware Specification | No | The paper mentions 'compute limitations' but does not specify the exact hardware used for running experiments (e.g., specific GPU/CPU models, memory details). |
| Software Dependencies | Yes | Our experiments are performed using the Llama2-7B model and its tokenizer ( meta-llama/Llama-2-7b-chat-hf ) available via the transformer package (v4.31.0). |
| Experiment Setup | Yes | Each sample is truncated to 1024-context length to accommodate for our compute limitations. ... No cross-validation is employed for hyper-parameter selection, and default parameters of the logistic regression and the random forest models from sklearn are used. |