https://github.com/rmanluo/llm-facteval
Source code of paper "Systematic Assessment of Factual Knowledge in Large Language Models" - EMNLP Findings 2023
https://github.com/rmanluo/llm-facteval
evaluation knowledge-graph llm trustfulness
Last synced: 26 days ago
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Source code of paper "Systematic Assessment of Factual Knowledge in Large Language Models" - EMNLP Findings 2023
- Host: GitHub
- URL: https://github.com/rmanluo/llm-facteval
- Owner: RManLuo
- Created: 2023-10-29T23:05:16.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-11-18T13:30:30.000Z (over 1 year ago)
- Last Synced: 2024-05-23T00:05:23.376Z (about 1 year ago)
- Topics: evaluation, knowledge-graph, llm, trustfulness
- Language: Python
- Homepage:
- Size: 73.2 KB
- Stars: 12
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LLM-facteval
Source code of paper ["Systematic Assessment of Factual Knowledge in Large Language Models"](https://arxiv.org/abs/2310.11638) - EMNLP Findings 2023
Our framework contains four main components:
- `kg`: declare how to read and preprocess knowledge graph
- `extractor`: to extract question triplets, nodes and relation summary from the knowledge graph.
- `generator`: to generate questions/answers from extracted triplets
- `evaluator`: to evaluate LLM's response
Check `certlm/registry.py` for the list of supported extractors, generators and evaluators
## Reproducibility
Please check [this document](scripts/README.md) for steps to reproduce our experiments in the paper.
## Adding new KGs to the framework
### Knowledge Graph (KG)
For a new KG dataset, it should extend the `certlm.kg_dataset.BaseKG` class and implement following methods:
- `load_relation()`: load all available relations to `self.relations`
- `get_input_relation_file(relation)`: return path to input relation file for a given relation
- `get_relation_name(relation)`: get relation label
- `get_relation_type(relation)`: return relation type: 1-1, N-1, N-M
Current supported KG:
- [LAMA](https://arxiv.org/pdf/1909.01066.pdf)
- trex
- google_re
- [BioLAMA](https://arxiv.org/pdf/2110.08173.pdf)
- umls
- wiki-bio
The preprocessed data can be found [here](https://drive.google.com/drive/folders/1Y17Hcnh9bjmOrxfU-R2ktgu444QQh9_2?usp=sharing).
### T-REx
Example of T-REx relation
```json
{
"relation": "P19",
"template": "[X] was born in [Y]",
"label": "place of birth",
"description": "most specific known (e.g. city instead of country, or hospital instead of city) birth location of a person, animal or fictional character",
"type": "N-1"
}
```
### Extractor
A triplet extractor for a KG should extend the `certlm.extractors.BaseExtractor` class and implement following methods:
- `extract_relation(relation, relation_input_file, output)`: extract relation data stored in `relation_input_file` and save to `output` file. In addition to the `output` file, a relation summary file is also created to index all the subject, object nodes which will be useful for evaluating N-M relations.
- `get_input_question_files(data_dir, relation)`: return path to questions and summary extracted from the given relation
**Note that, we should standardize the relation info to follow this format for reusability**
The question output is a jsonl file where each line is a json with following structure:
```json
{
"subject_label": subject_label,
"object_label": object_label,
"object_uri": object_uri,
"subject_uri": subject_uri,
"relation_info": {
"relation": "P19",
"template": "[X] was born in [Y]",
"label": "place of birth",
"description": "most specific known (e.g. city instead of country, or hospital instead of city) birth location of a person, animal or fictional character",
"type": "N-1",
"subject_symbol": "[X]",
"object_symbol": "[Y]"
}
}
```
The relation summary should have following structure
```json
{
"relation_info": {
"relation": "P19",
"template": "[X] was born in [Y]",
"label": "place of birth",
"description": "most specific known (e.g. city instead of country, or hospital instead of city) birth location of a person, animal or fictional character",
"type": "N-1",
"subject_symbol": "[X]",
"object_symbol": "[Y]"
},
"node_summary": {
"objects": {
"uri1": {
"object_label": object_label,
"object_uri": object_uri,
"subjects": [array of subject uri]
},
...
}
},
"subjects": {
"uri1": {
"subject_label": subject_label,
"subject_uri": subject_uri,
"objects": [array of object uri]
},
...
}
}
}
```
Example of running command
```bash
python run_certlm.py --step extract \
--kg trex \
--data-dir ./examples/TREx \
--data-file relations.jsonl \
--output-dir ./output
```
### Generator
We support the following generator: `template`, `llm-mask`, `llm-question`. Each generator needs to implement the following methods
- `generate_questions(triplet_input_file, relation_summary_file, output_file)`
```python
# question
prompts = [
{"role": "system", "content": prompt},
{"role": "user", "content": content}
]
expected_answers = [{"uri": a[0], "label": a[1]} for a in answers]
question_record = {
"question_id": question_id,
"prompts": prompts,
"answers": expected_answers,
}
json_record = json.dumps(question_record)
fout.write(json_record + '\n')
```
Example of running command
```bash
python run_certlm.py --step question_generate \
--kg trex \
--generator masking \
--data-dir ./examples/TREx \
--data-file relations.jsonl \
--gen-input-dir ./output/tuples
```
# Citation
If this repo is useful for your own research, please cite us with the following bibtex entry
```
@article{luo2023systematic,
title={Systematic Assessment of Factual Knowledge in Large Language Models},
author={Luo, Linhao and Vu, Thuy-Trang and Phung, Dinh and Haffari, Gholamreza},
journal={Findings of EMNLP},
year={2023}
}
```