Swinburne University of Technology

Amir Aryani

Integrating Large Language Models (LLMs) such as GPT into organizations’ data workflows is a complex process with various challenges. These obstacles include but are not limited to technical, operational, ethical, and legal dimensions, each presenting hurdles that organisations must navigate to harness the full potential of LLMs effectively.

What are the challenges in integrating LLMs into organisations' data workflows?

I asked Generative AI Models about their context window. Their response was intriguing. The context window for a large language model (LLM) like OpenAI’s GPT refers to the maximum amount of text the model can consider at any one time when generating a response. This includes both the prompt provided by the user and the model’s generated text.

What is your Context Window?

Authors: Hui Yin, Amir Aryani  As we discussed in our previous article “A Brief Introduction to Retrieval Augmented Generation (RAG)”, RAG is an artificial intelligence framework that incorporates the latest reliable external knowledge and aims to improve the quality of responses generated by pre-trained language models (PLM). Initially, it was designed to improve the performance of knowledge-intensive NLP tasks (Lewis et al., 2020). As

Navigating the Present: Exploring Practical Horizons of Retrieval-Augmented Generation (RAG)

At the time of writing this post (November 19, 2023), there are 401,014 models and transformers listed on Huggingface.co. This number is a testament to the success and impact of the open-source model in the development of AI technologies. However, the challenge at hand is understanding the available models and keeping up to date with the latest developments.

Estimated time: 12 Years to Review all 400k Models on Hugging Face

Prompt engineering is a technique used to effectively communicate with large language models (LLM) like GPT-3 or GPT-4 and get the desired output. Here there are applications of 8 distinct prompt techniques to interact with Mistral AI and find out how we can prompt effectively to learn about Foundation Models.
<strong>
 Zero-Shot Learning
</strong>
: This involves giving the AI a task without any prior examples.

8 Types of Prompt Engineering

There is a plethora of articles and predictions about the future development of AI. Reading through them suggests three main areas of high-impact changes that would transform our daily lives: Healthcare, Automation, and Manufacturing. The impact of AI in these industries is profound and multifaceted, with each sector experiencing unique transformations. Some of these ideas are described in this NVIDIA article by Cliff Edwards.

Revolutionizing Wellness and Wheels: AI’s Transformative Role in Health and Automotive Industries

Procrastination, a common adversary in our daily lives, often leads to a backlog of tasks and ambitions. It’s a silent thief, stealthily stealing opportunities and time. You might have a stack of books on your bookshelf, longing to be read. Perhaps there’s a new language you’ve wanted to learn, or piles of paperwork cluttering your office that you’ve been meaning to organize.

Maximizing Productivity in 10-Minute Bursts

Authors: Hui Yin , Amir Aryani  With the increasing application of large language models in various scenarios, people realize that these models are not omnipotent. When generating dialogues (Shuster et al., 2021), the models often produce hallucinations, leading to inaccurate answers.

A Brief Introduction to Retrieval Augmented Generation (RAG)

Stories by Amir Aryani on Medium

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*MYULJWGydnlht7zzLUtThg.png" />
<figcaption>Created with DALLE On 18 Jan 2024</figcaption>
</figure>

Authors: [Hui Yin](https://www.linkedin.com/in/hui-yin-784538213/) ,
[Amir Aryani](https://www.linkedin.com/in/amiraryani/)

With the increasing application of large language models in various
scenarios, people realize that these models are not omnipotent. When
generating dialogues (Shuster et al., 2021), the models often produce
hallucinations, leading to inaccurate answers. Despite these large
language models storing impressive knowledge within their parameters of
a neural network, these hallucinated facts are frozen at the point of
model training, making it infeasible to retrain the model. If we can
bootstrap the model correctly, it can still be a powerful tool, and here
comes the Retrieval Augmented Generator (RAG).

Retrieval-augmented generation (RAG) is an AI framework designed to
enhance the quality of responses generated by large language models
(LLM). It leverages external knowledge sources, typically knowledge
graphs or other knowledge bases, such as Wikipedia, to augment the
generation capabilities of these models. This integration enables the
model to incorporate additional external information during text
generation, thereby improving its accuracy and applicability while
ensuring access to the latest reliable information.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*vvYZftOCIMiBWZxZGuA89A.png" />
<figcaption>RAG Architecture Model</figcaption>
</figure>

The retrieval-augmented generator (RAG) was initially introduced in
"REALM: Retrieval-Augmented Language Model Pretraining" by Guu et al.,
(2020), where they discuss the utilization of dense retrievers to
enhance language model pre-training. In REALM, the retriever accesses
knowledge from a textual knowledge corpus, (e.g., all of Wikipedia), and
then the signal from the generator backpropagates through the retriever.
They verified the effectiveness of the proposed Retrieval-Augmented
Language Model pre-training (REALM) on the challenging Open-domain
Question Answering (Open-QA) task. The results demonstrated its
superiority over three benchmarks by a substantial margin (4--16%
absolute accuracy), and it also showcased improvements in
interpretability and modularity.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*7GvNzgFWvViZTouSoR6S9w.png" />
<figcaption>REALM Structure</figcaption>
</figure>

Structure of REALM: BERT is employed to encode queries and documents
separately, and [Maximum Inner Product Search
(MIPS)](https://en.wikipedia.org/wiki/Maximum_inner-product_search) is
utilized to calculate the inner product between query embeddings and
document embeddings, identifying the most relevant documents.
Subsequently, the concatenated query and selected documents are input
into the knowledge-augmented encoder (BERT pre-training) as new input,
and the final result is generated.

Another remarkable work is "Retrieval-Augmented Generation for
Knowledge-Intensive NLP Tasks" by Lewis et al., (2020). They constructed
a pre-trained retriever consisting of a Query encoder and a Document
index. For example, [Maximum Inner Product Search
(MIPS)](https://en.wikipedia.org/wiki/Maximum_inner-product_search) was
employed to identify the top-K documents. The generator component of the
system was a pre-trained
[seq2seq](https://en.wikipedia.org/wiki/Seq2seq) model. The entire model
was fine-tuned end-to-end. In their approach for final predictions, they
treated k (the number of documents selected by the retriever) as a
latent variable. This allowed them to control the number of outputs
predicted by the [seq2seq](https://en.wikipedia.org/wiki/Seq2seq) model.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*5_w3_Gctd8hClmYkrqNpwg.png" />
<figcaption>Maximum Inner Product Search (MIPS)</figcaption>
</figure>

Overview of the structure: The retriever combines QueryEncoder and
Document Index, and the generator consists of a pre-trained
[seq2seq](https://en.wikipedia.org/wiki/Seq2seq) model fine-tuned
end-to-end. Maximum Inner Product Search (MIPS) is utilized to find the
top-k similar documents. For the final predictions, 'k' acts as a latent
variable, and marginalises over
[seq2seq](https://en.wikipedia.org/wiki/Seq2seq) predictions given
different documents.

The above two works have inspired more researchers to study RAG. Based
on the basic structure shown in the first figure, specific
implementation methods are shown in other works. These existing works
all demonstrate RAG's contribution to LLM in the following aspects:

- Utilisation of the latest external knowledge,
- Flexibility and adaptability,
- Quality improvement,
- Interpretability,
- Modularity, and
- Model size reduction.

From an application perspective, the original RAG was oriented to NLP
tasks such as Open-QA (Mao et al., 2020) and dialogue (Thulke et al.,
2021) and is now also used for image synthesis (Blattmann et al., 2022)
and text-to-image (Chen et al., 2022).

RAG possesses many advantages; however, it's crucial to acknowledge that
leveraging an external document database to enhance the generation
process involves computing the similarity between the query embedding
and external document embeddings, followed by selecting the top-k
documents. This introduces substantial computational challenges. As the
size of the external document database increases, the computational cost
of calculating similarity and determining the top-k documents grows
significantly. Achieving scalability, especially when processing
numerous documents, demands efficient algorithms and parallel processing
capabilities to make the retrieval process feasible within a reasonable
timeframe. Additionally, storing and accessing large external databases
can necessitate significant memory resources. For applications requiring
real-time responses, the time taken for retrieval becomes a critical
factor, emphasizing the need to balance computational complexity with
response time. Hence, delving into optimization and approximation
methods or incorporating specialized hardware emerges as promising
avenues for future research.

As an auxiliary to LLM, it is foreseeable that more people will devote
their energy to exploring various applications of RAG.

**Acknowledgement**

This work was supported by the Australian Government through the
Australian Research Council's Industrial Transformation Training Centre
for Information Resilience (CIRES) project number IC200100022.

Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021).
Retrieval augmentation reduces hallucination in conversation.
<https://arxiv.org/abs/2104.07567>

Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M. (2020). Retrieval
Augmented Language Model Pre-Training. In *Proceedings of the 37th
International Conference on Machine Learning, PMLR* (Vol. 119, pp.
3929--3938). [](https://arxiv.org/abs/2005.11401) Available from
<https://proceedings.mlr.press/v119/guu20a.html>

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N.,
Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., &
Kiela, D. (2020b). Retrieval-Augmented Generation for
Knowledge-Intensive NLP Tasks. Neural Information Processing Systems 33
(NeurIPS 2020). <https://arxiv.org/abs/2005.11401>

Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2020).
Generation-Augmented Retrieval for Open-domain Question Answering.
<https://doi.org/10.48550/arXiv.2009.08553>

Thulke, D., Daheim, N., Dugast, C., & Ney, H. (2021). Efficient
Retrieval Augmented Generation from Unstructured Knowledge for
Task-Oriented Dialog. Accepted by DSTC9 Workshop at AAAI-2021.
<https://doi.org/10.48550/arXiv.2102.04643>

Blattmann, A., Rombach, R., Oktay, K., Müller, J., & Ommer, B. (2022).
Retrieval-Augmented Diffusion Models. In *Advances in Neural Information
Processing Systems 35 (NeurIPS 2022) .*
<https://doi.org/10.48550/arXiv.2204.11824>

Chen, W., Hu, H., Chen, X., Verga, P., & Cohen, W. W. (2022). MuRAG:
Multimodal Retrieval-Augmented Generator for Open Question Answering
over Images and Text. *Accepted to EMNLP 2022 main conference*.
<https://doi.org/10.48550/arXiv.2210.02928>

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