polygraphLLM 0.1.0

PolygraphLLM creates building blocks for generic approaches for hallucination detection in Large Language Models (LLMs).

In the context of LLMs, hallucination refers to the generation of text that includes information or details that are not supported by the input or context provided to the model. Hallucinations occur when the model produces text that is incorrect, irrelevant, or not grounded in reality based on the input it receives.

PolygraphLLM is intended to help in the detection of hallucinations.

Installation

pip install polygraphLLM

The source code is currently hosted on GitHub at: https://github.com/cisco-open/polygraphLLM

Export envs and install a small SpaCy model

export OPENAI_API_KEY=
export SERPER_API_KEY=
python3 -m spacy download en_core_web_sm

Usage

from polygraphLLM import Detector
detector = Detector()

Requesting results from the LLM

responses = detector.ask_llm(
'Which Lactobacililus casei strain does not have the cholera toxin subunit A1 (CTA1) on the surface?',
n=2, # the number of responses
temperature=0.5, # temperature give to the LLM
max_new_tokens=100 # number of tokens for response
)
print(responses)

Extract triplets from a text. (subject, predicate, object)

triplets = detector.extract_triplets(
'Which Lactobacililus casei strain does not have the cholera toxin subunit A1 (CTA1) on the surface?,
)
print(triplets)

Extract sentences from a text.

sentences = detector.extract_sentences(
'There is no specific Lactobacillus casei strain that is known to not have the cholera toxin subunit A1 (CTA1) on its surface.'
'However, some strains may have a lower expression of CTA1 or may not have the gene for CTA1 at all. '
'The presence or absence of CTA1 on the surface of Lactobacillus casei strains can vary depending on the specific strain and its genetic makeup.',
)
print(sentences)

Generate question from a given text.

question = detector.generate_question(
'There is no specific Lactobacillus casei strain that is known to not have the cholera toxin subunit A1 (CTA1) on its surface.'
'However, some strains may have a lower expression of CTA1 or may not have the gene for CTA1 at all. '
'The presence or absence of CTA1 on the surface of Lactobacillus casei strains can vary depending on the specific strain and its genetic makeup.',
)
print(question)

Retrieve information from the internet for a list of inputs

results = detector.retrieve(
['What factors can affect the presence or absence of the cholera toxin subunit A1 on the surface of Lactobacillus casei strains?'],
)

print(results)

Check the hallucination scores using the triplets.

question = 'What factors can affect the presence or absence of the cholera toxin subunit A1 on the surface of Lactobacillus casei strains?'
answer = detector.ask_llm(question, n=1)[0]
triplets = detector.extract_triplets(answer)
reference = detector.retrieve([question])
results = [
detector.check(t, reference, answer, question=question)
for t in triplets
]
print(results)

Check the similarity of texts using bert score.

question = 'What factors can affect the presence or absence of the cholera toxin subunit A1 on the surface of Lactobacillus casei strains?'
answers = detector.ask_llm(question, n=5)
first_answer = answers[0]
sentences = detector.extract_sentences(first_answer)
sentences = [s.text for s in sentences]
sampled_passages = answers[1:]
results = detector.similarity_bertscore(sentences, sampled_passages)
scores = float("{:.2f}".format(sum(results)/len(results)))
print(scores)

Check the similarity of texts using nGram model.

passage = "Michael Alan Weiner (born March 31, 1942) is an American radio host. He is the host of The Savage Nation."
sentences = detector.extract_sentences(passage)
sentences = [s.text for s in sentences]

sample1 = "Michael Alan Weiner (born March 31, 1942) is an American radio host. He is the host of The Savage Country."
sample2 = "Michael Alan Weiner (born January 13, 1960) is a Canadian radio host. He works at The New York Times."
sample3 = "Michael Alan Weiner (born March 31, 1942) is an American radio host. He obtained his PhD from MIT."

results = detector.similarity_ngram(sentences, passage, [sample1, sample2, sample3])
scores = float("{:.2f}".format(results['doc_level']['avg_neg_logprob']))

print(scores)

Building blocks

This project implements generic approaches for hallucination detection.

The Detector base class implements the building blocks to detect hallucinations and score them.

ask_llm - method to request N responses from an LLM via a prompt

extract_triplets - method to extract subject, predicate, object from a text.

extract_sentences - method to split a text into sentences using spacy

generate_question - method to generate a question from a text

retrieve - method to retrieve information from google via the serper api

check - method to check if the claims contain hallucinations

similarity_bertscore - method to check the similarity between texts via bertscore

similarity_ngram - method to check the similarity between texts via ngram model

You can implement any custom detector and combine all the available methods from above.

References

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

https://arxiv.org/abs/2303.16634

Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models

https://arxiv.org/abs/2303.08896

RefChecker for Fine-grained Hallucination Detection

https://github.com/amazon-science/RefChecker

Chainpoll: A high efficacy method for LLM hallucination detection

https://arxiv.org/abs/2310.18344

Can LLMs express their uncertainty? An empirical evaluation of confidence elicitation in LLMs

https://openreview.net/pdf?id=gjeQKFxFpZ

Self-contradictory hallucinations of LLMs: Evaluation, detection and mitigation

https://arxiv.org/pdf/2305.15852

Contributing

Any contributions you make are greatly appreciated. For detailed contributing instructions, please check out Contributing Guidelines.

License

Apache License 2.0.