Skip to content

Latest commit

 

History

History
116 lines (85 loc) · 7 KB

README.md

File metadata and controls

116 lines (85 loc) · 7 KB
Raw

🦉 RAG-Ex: A generic framework for explaining Retrieval Augmented Generation

Recent advancement of large language models (LLMs) has led to their seamless adoption in real-world applications, including Retrieval Augmented Generation (RAG) in Question Answering (QA) tasks. However, owing to their size and complexity, LLMs offer little to no explanations for why they generate a response, given the input. Such a "black-box" ascription of LLMs effectively reduces the trust and confidence of end users in the emerging LLM-based RAG systems. In this work, we introduce RAG-Ex, a model- and language-agnostic explanation framework that presents approximate explanations to the users revealing why the LLMs possibly generated a piece of text as a response, given the user input. Our framework is compatible with both open-source and proprietary LLMs. We report the significance scores of the approximated explanations from our generic explainer in both English and German QA tasks and also study their correlation with the downstream performance of the LLMs. In the extensive user studies, we observed that our explainer yields an F1-score of 76.9% against the end user annotations and attains almost in-par performance with model-intrinsic approaches.

readme_banner.png

Find the demo application here.

How to use RAG-Ex?

Pre-requisites

We recommend Python version >= 3.8

pip install -r requirements.txt
python -m spacy download en_core_web_sm
python -m spacy download de_core_news_sm

Step 1: Start the Language Model Service (LMS)

  • Follow the instructions to install the LMS by checking out this repo.
  • Ensure the service is running at port 9985 such that http://localhost:9985 is available.

Step 2: Start RAG-Ex

You can start the UI for RAG-Ex with: python -m xlm.start.

Step 3: Use RAG-Ex

The most important settings are as follows:

Question

Type your input here or pick one of the examples above.

Explanation Granularity

This sets the granularity of the explanations. E.g. WORD_LEVEL splits the output into words and gives an importance score for every word. SENTENCE_LEVEL collects every sentence and its importance score.

Importance Bounds

The Feature importance is between 0.0 and 1.0. The higher, the more important was the sentence / word / ... of the input to generate the output. The bound settings determine, from which importance score a feature is highlighted. E.g. keeping the default settings (Upper=85, Middle=75, Lower=10) means that features with an importance higher than 0.85 are highlighted in green and features with an importance higher than 0.75 are highlighted yellow.

Model

The model that is used for all text generations. This model is then used to both generate the original answer and the answers of all perturbed inputs.

Perturber

Here, we determine how to perturb the input. If the granularity is SENTENCE_LEVEL, the following holds:

Perturber Explanation
leave_one_out For every perturbed input, we leave one sentence out.
random_word_perturber We insert different random words in and around the corresponding sentence.
entity_perturber Each entity in the sentences is replaced by random words.
reorder_perturber The sentence is reordered.
antonym_perturber One or more words in each sentence is replaced with their antonyms, if they exist.
synonym_perturber Same as antonym_perturber, but we replace with synonyms.

Comparator

The comparator determines how the gap between the original output and the perturbed output(s) is measured.

Comparator Explanation
sentence_transformers_based_comparator We take the SBERT embeddings and do a cosine similarity.
levenshtein_comparator This counts, how many deletions, insertions and substitution of words within a sentence have to
be done to turn sentence A into sentence B.
jaro_winkler_comparator Similar to the levenshtein_comparator, while rewarding prefixes, i.e. when two sequences have
the same start.
n_gram_comparator The sentences are split into adjacent word chunks of length n. The more chunks match, the closer the
sentences are.

Citation

To use RAG-Ex in your publication, please cite it by using the following BibTeX entry.

@inproceedings{10.1145/3626772.3657660,
    author = {Sudhi, Viju and Bhat, Sinchana Ramakanth and Rudat, Max and Teucher, Roman},
    title = {RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Generation},
    year = {2024},
    isbn = {9798400704314},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3626772.3657660},
    doi = {10.1145/3626772.3657660},
    abstract = {Owing to their size and complexity, large language models (LLMs) hardly explain why they generate a response. This effectively reduces the trust and confidence of end users in LLM-based applications, including Retrieval Augmented Generation (RAG) for Question Answering (QA) tasks. In this work, we introduce RAG-Ex, a model- and language-agnostic explanation framework that presents approximate explanations to the users revealing why the LLMs possibly generated a piece of text as a response, given the user input. Our framework is compatible with both open-source and proprietary LLMs. We report the significance scores of the approximated explanations from our generic explainer in both English and German QA tasks and also study their correlation with the downstream performance of LLMs. In the extensive user studies, our explainer yields an F1-score of 76.9\% against the end user annotations and attains almost on-par performance with model-intrinsic approaches.},
    booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages = {2776–2780},
    numpages = {5},
    keywords = {explainability, large language models, retrieval augmented generation},
    location = {Washington DC, USA},
    series = {SIGIR '24}
}