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AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

This repository is the official implementation of AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph.

Project License Python 3.9+ OpenCompass

Model
An illustration of our AbsPyramid benchmark.

Requirements

Python version is 3.8.5

requirements:

bert_score==0.3.13
datasets==2.13.1
evaluate==0.4.1
llmtuner==0.2.3
numpy==1.24.4
pandas==2.0.3
peft==0.5.0
rouge_score==0.1.2
scikit_learn==1.3.0
torch==2.0.1
transformers==4.34.0

You can install all requirements with the command

pip install -r requirements.txt

Datasets

AbsPyramid

We build the AbsPyramid benchmark with two tasks: abstraction detection and abstraction generation. There data are released on HuggingFace Hub

In each of "Detection" and "Generation," we put data of all relations in the merged_dataset folder. Also, we put data of Noun-Entail, Verb-Entail, and Event-Entail in the noun_dataset, verb_dataset, and event_dataset folders, respectively.

Other sources

We also conduct experiments on Levy/Holt dataset and AbstractATOMIC dataset. The original links are Levy/Holt dataset and AbstractATOMIC. To be consistent when loading data, we also transform their datasets into the jsonl format of our dataset: Levy/Holt and AbstractATOMIC. All licenses are subject to their original releases.

Training Models

Models for Abstraction Detection

The scripts to fine-tune language models:

  1. If you want to fine-tune pretrained language models, such as BERT, RoBERTa, and DeBERTa, the code is in PLM_FT_main.py. The shell script shell_script/run_PLM_FT.sh sets most parameters for you. We include commands to call the shell script in shell_script/command_detection.sh.
  2. If you want to fine-tune LLMs with LoRA, such as Llama2 (13B), the code is in LLM_LORA_FP16.py. The shell script shell_script/run_LLM_LORA.sh sets most parameters for you. We include commands to call the shell script in shell_script/command_detection.sh.
  3. If you want to test NLI models in zero-shot setup, such as BART-large-mnli, the code is in NLI_ZERO_SHOT.py. The shell script shell_script/run_NLI_ZERO.sh sets most parameters for you. We include commands to call the shell script in shell_script/command_detection.sh.
  4. If you want to fine-tune NLI models, such as BART-large-mnli, the code is in NLI_FT_main.py. The shell script shell_script/run_NLI_FT.sh sets most parameters for you. We include commands to call the shell script in shell_script/command_detection.sh.

Models for Abstraction Generation

If you want to train models to generate abstract concepts, the code is in generator_main.py. We provide shell scripts for training and inference of generation models in shell_script/run_gen_LLM.sh and shell_script/run_gen_LLM_inference.sh. Also, commands to call those scripts are shown in shell_script/command_generation.sh

Contributing

This repo is maintained by Zhaowei Wang