tresiwald/cross-topic-probing

Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization

This repository includes the code for running the experiments to probe in the In- and Cross-Topic experimental settings.

Further details can be found in our publication Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization

Abstract: Pre-trained Language Models (PLMs) show impressive success when learning downstream tasks. Pre-trained language models (LMs) perform well in In-Topic setups, where training and testing data come from the same topics. However, they face challenges in Cross-Topic scenarios where testing data is derived from distinct topics - such as Gun Control. This study analyzes various LMs with three probing-based experiments to shed light on the reasons behind the In- vs. Cross-Topic generalization gap. Thereby, we demonstrate, for the first time, that generalization gaps and the robustness of the embedding space vary significantly across LMs. Additionally, we assess larger LMs and underscore the relevance of our analysis for recent models. Overall, diverse pre-training objectives, architectural regularization, or data deduplication contribute to more robust LMs and diminish generalization gaps. Our research contributes to a deeper understanding and comparison of language models across different generalization scenarios.

Contact person: Andreas Waldis, [email protected]

https://www.ukp.tu-darmstadt.de/

https://www.tu-darmstadt.de/

Don't hesitate to e-mail us or report an issue if something is broken (and it shouldn't be) or if you have further questions.

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

Project structure

(change this as needed!)

• data/ -- directory for the data
• probes/ -- directory for the data
• src -- contains all necessary python files

Requirements

This repository requires Python3.6 or higher; further requirements can be found in the requirements.txt. Further, it need the spacy model en_core_web_sm. This repository requires a running MLFLOW instance for reporting and Dropbox as storage for computed models. Please define the corresponding URL and Dropbox auth-token in the file src/defs/default_config.py

Data

We make use of the UKP ArgMin dataset Stab et al. 2018 and the WTWT dataset Conforti et al. 2020. Once you have obtained both datasets, put them in the folder folder.

Generate probing tasks

To generate the Cross-Topic probing task, run the following commands:

$ parse_task_cross_topic.py --task ukp-argmin 
$ parse_task_cross_topic.py --task wtwt

To generate the Cross-Topic topic information classification tasks, run the following commands:

$ parse_task_cross_topic_tokens.py --task ukp-argmin 
$ parse_task_cross_topic_tokens.py --task wtwt

To generate the In-Topic probing tasks, run the following commands:

$ convert_cross_topic_tasks.py

To generate the control tasks, run the following commands:

$ convert_control_task.py

Experiments I

To run the first experiments on a model - like bert-base-uncased - run the following command.

$ run-probes.py --task ukp-argmin --model bert-base-uncased --seeds 0,1,2 --out_filter token-types --amnesic False
$ run-probes.py --task ukp-argmin-in --model bert-base-uncased --seeds 0,1,2 --out_filter token-types --amnesic False
$ run-probes.py --task wtwt --model bert-base-uncased --seeds 0,1,2 --out_filter token-types --amnesic False
$ run-probes.py --task wtwt-in --model bert-base-uncased --seeds 0,1,2 --out_filter token-types --amnesic False

Experiments 2

To run the second experiment on a model - like bert-base-uncased - run the following command to fit the amnesic probe.

$ run-probes.py --task ukp-argmin --model bert-base-uncased --seeds 0,1,2 --in_filter token-types --amnesic True
$ run-probes.py --task ukp-argmin-in --model bert-base-uncased --seeds 0,1,2 --in_filter token-types --amnesic True
$ run-probes.py --task wtwt --model bert-base-uncased --seeds 0,1,2 --in_filter token-types --amnesic True
$ run-probes.py --task wtwt-in --model bert-base-uncased --seeds 0,1,2 --in_filter token-types --amnesic True

Afterward we can run probes again without topic information, for example, NER for Cross-Topic on ukp-argmin

$ run-probes-without-property.py --amnesic_experiment probes-amnesic-ukp-argmin-token-types-40-cls-topic-maj --target_experiment probes-ukp-argmin-ner

Experiments 3

To run the third experiment, a fine-tuned model (fold=0, seed=0) - like bert-base-uncased-ft-ukp-argmin-0-0 - runs the following command. Note you need to provide the fine-tuned model as a folder in the running directory.

$ evolution-dropbox.py --task ukp-argmin --model_name bert-base-uncased-ft-ukp-argmin-0-0 --seed 0 --fold 0