This repository contains code for our VeriScore factuality metric, which is a pipelined approach with three steps: (1) claim extraction, (2) evidence retrieval, and (3) claim verification. This package can be run with either closed-source LLMs (requires OpenAI/Anthropic API key), or with fine-tuned models for claim extraction and verification that you can download and use. The evidence retrieval step is performed with Google Search via the Serper API, so you will need to get a Serper API key here to use VeriScore.

Please see our Colab notebook for a demo!

You can choose between prompting OpenAI/Anthropic models or using our fine-tuned models via the model_name option. If you specify the path to the checkpoint of a fine-tuned model, it will automatically access the local model for the inference. If the model name is not in this format, it will use an API call instead.

1. Make a new Python 3.9+ environment using virtualenv or conda.
2. Install veriscore pacakge using pip
3. Download en_core_web_sm using spacy library
4. Our code supports inference using fine-tuned models based on the Unsloth library. To use this feature, you need to install the Unsloth library. If you use conda, make sure to follow the conda-specific instructions from Unsloth.

pip install --upgrade veriscore
python -m spacy download en_core_web_sm

1. Download prompt folder that contains txt files of the prompt templates (see the prompt folder in this repository)
2. Add an OpenAI or Claude API key to an environment variable in your bash for the prompting approach

export OPENAI_API_KEY_PERSONAL={your_openai_api_key}
export CLAUDE_API_KEY={your_claude_api_key}

3. Set SERPER API key to an environment variable in your bash for evidence retrieval

export SERPER_KEY_PRIVATE={your_serper_api_key}

4. For the prompt-based approach, you need to set data_dir/demos/ with few-shot examples.

This is an end-to-end pipeline for running VeriScore.

 python3 -m veriscore.veriscore --data_dir {data_dir} --input_file {input_file} --model_name_extraction {model_name_extraction} --model_name_verification {model_name_verification}

• data_dir: Directory containing input data. ./data by default.
• input_file: Name of the input data file. It should be in the jsonl format where each line contains
  • question(optional): A query to prompt a language model for an output. If question key is provided, the model automatically adapts to QA prompting. If not, it extracts claims using non-QA prompting.
  • response: An output generated by the language model given the question
  • model: Name of the model that generated the response
  • prompt_source: Name of the dataset from where the question is from (e.g., FreshQA)
• model_name_extraction: Name of the model used for claim extraction; gpt-4-0125-preview by default.
• model_name_verification: Name of the model used for claim verification; gpt-4o by default.
• use_external_extraction_model: If specified, it uses your custom model instead of the one from the API call. We use Unsloth for the fine-tuned model. False by default.
• use_external_verification_model: If specified, it uses your custom model instead of the one from the API call. We use Unsloth for the fine-tuned model. False by default.

Other optional flags:

• output_dir: Directory for saving ouptut data. ./data by default.
• cache_dir: Directory for saving cache data. ./data/cache by default.
• label_n: This is the type of label for claim verification. It could be 2 (binary) or 3 (ternary)
  • 2: supported and unsupported
  • 3: supported, contradicted, and inconclusive
• search_res_num: A Hyperparameter for number of search result. 10 by default.

Saving output: input_file_name is file name removed jsonl from —-input_file

• extracted claims will be saved to output_dir/claims_{input_file_name}.jsonl.
• searched evidence will be saved to output_dir/evidence_{input_file_name}.jsonl.
• verified claims will be saved to output_dir/model_output/verification_{input_file_name}.jsonl.

Claim extraction:

 python3 -m veriscore.extract_claims --data_dir {data_dir} --input_file {input_file} --model_name {model_name} 

• input_file: Name of input data file. It should be jsonl format where each line contains
  • question(optional): A query to prompt a language model for an output. If question key is provided, the model automatically adapts to QA prompting. If not, it extracts claims using non-QA prompting.
  • response: An output generated by the language model given the question
  • model: Name of the model that generated the response
  • prompt_source: Name of the dataset from where the question is from (e.g., FreshQA)
• model_name: Name of model used for claim extraction. gpt-4-0125-preview by default.
• use_external_model: If specified, it uses your custom model instead of using API calls. We use Unsloth for the fine-tuned models. False by default.

output:

 {
  "question": question.strip(),
  "prompt_source": prompt_source,
  "response": response.strip(),
  "prompt_tok_cnt": prompt_tok_cnt,
  "response_tok_cnt": response_tok_cnt,
  "model": model,
  "abstained": False,  
  "claim_list": list of claims for each snippet,
  "all_claims": list of all claims
 }

Evidence searching:

 python3 -m veriscore.retrieve_evidence --data_dir {data_dir} --input_file {input_file}

• input_file: Name of input data file. It should be in the jsonl format where each line contains the keys of the output dictionary from the Claim extraction. output:

 {
  ...
  "claim_snippets_dict": dictionary for claim and list of searched evidence. each evidence have dictionary of {"title": title, "snippet": snippet, "link": link}
 }

Claim verification:

 python3 -m veriscore.verify_claims --data_dir {data_dir} --input_file {input_file} --model_name {model_name}

• input_file: Name of input data file. It should be jsonl format where each line contains the keys of the output dictionary from the Evidence searching.
• use_external_model: If specified, it uses your custom model instead model used by API call. We use Unsloth to inference from specified model. False by default.

output:

 {
  ...
  "claim_verification_result":[
    {
     "claim": claim text,
     "search_results": concatenated search result
     "verification_result": verification label
 }

This Google Drive folder contains:

• the long-form generations of the tested models given the prompts in Table 1 in the paper
• human annotation results in Section 3.3