Top 6 Approaches to Overcome the LLMs Limitations

0. Introduction

Large Language Models (LLMs) like ChatGPT have revolutionized various industries with their advanced capabilities in processing and generating human-like text. However, it's crucial to recognize their inherent limitations. 

While proficient in tasks like summarization and creative draft generation, these models can struggle with issues like generating reliable text (often leading to hallucinations), limited reasoning abilities, especially in complex mathematical contexts, and vulnerabilities to security threats. 

To learn more about these limitations, refer to our detailed blog. 

This article focuses on the top six approaches to effectively overcome these challenges and ensure LLMs are utilized to their fullest potential responsibly and efficiently.

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1. Approach 1: Prompting

1.1 Definition

Prompt engineering involves framing queries for language models in a way that guides them to produce the most relevant and accurate responses. It's a strategic process ensuring clarity, context, and precision in the questions.

1.2 Example of Illustration

Consider a query about summarizing a scientific article. A basic prompt like "Summarize this article" can be vague. An engineered prompt would be more specific: "Summarize this article in three key points focusing on its methodology, findings, and implications."

1.3 Link to Read More

For a comprehensive guide on prompt engineering and strategies to test changes systematically, visit our detailed article about: What are Different Prompt Strategies?

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2. Approach 2: Functions

2.1 Definition

Functions are external capabilities that LLMs can leverage to perform tasks beyond their inherent abilities, such as data retrieval or executing complex operations.

2.2 Example of Illustration

An example is using the DALL-E function to create images. When GPT-4 is prompted to generate a picture, it cannot natively output this picture. It calls upon DALL-E to produce the visual content based on descriptive text input.

2.3 Link to Read More

For a detailed understanding of how LLMs can use functions, see MemGPT's research at MemGPT Research, Toolformer by Meta, and the guide on Function Calling with LLMs.

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3. Approach 3: Retrieval-augmented Generation

3.1 Definition

Retrieval-augmented generation (RAG) is a technique that combines the generative power of LLMs with external databases to enhance the model's responses with up-to-date and detailed information.

3.2 Example of Illustration

For instance, when an LLM is asked about the latest advancements in renewable energy, RAG would enable it to first pull the most recent research data from a scientific database, ensuring that the response not only reflects the LLM's base knowledge but also includes the latest findings in the field.

Consider another scenario - an LLM tasked with providing financial advice. Using RAG, it queries the latest stock market data and expert analysis from a financial database before responding, thus offering current and reliable investment insights beyond its initial training data, which does not contain the latest market news and trends.

3.3 Links to Read More

For an in-depth exploration of RAG, visit our detailed article about: What is Retrieval-Augmented Generation (RAG)?

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4. Approach 4: Knowledge Distillation

4.1 Definition

Knowledge Distillation is a technique for streamlining complex models. It enables smaller models to emulate the performance of larger ones by training on their outputs and optimizing computational resources without significantly compromising accuracy.

4.2 Example of Illustration

A practical application of Knowledge Distillation is evident in developing the phi-1 language model. Phi-1, a specialized Transformer model for Python coding, leverages 1.3 billion parameters and is fine-tuned with diverse coding data, including actual Python code, StackOverflow Q&As, and synthetic exercises generated by GPT-3.5. 

Despite its smaller size, phi-1 harnesses high-quality data and synthetic exercises from a more extensive model to achieve remarkable accuracy in coding benchmarks (achieves over 50% accuracy on Python coding evaluations,) showcasing the effectiveness of Knowledge Distillation and surpasses (5x) larger models.

4.3 Links to Read More:

For an in-depth exploration of Knowledge Distillation, please check our in-depth article on Knowledge Distillation.

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5. Approach 5: Fine Tuning

5.1 Definition

Fine tuning is a process where a pre-trained model is adapted to a specific task by continuing the training phase and adjusting the model weights based on a particular dataset. This technique leverages the knowledge the model has already gained during its initial broad training and focuses it on the nuances of a targeted application.

5.2 Example of Illustration

Consider a language model trained on general English text. To fine-tune it for legal document analysis, it would be further trained on a dataset of legal documents. This specialized training adjusts the model’s weights to understand better and generate text in legal contexts, improving its performance on tasks like contract analysis or case prediction.

5.3 Links to Read More

The following paper provides extensive research and findings to explore fine-tuning methods and their impact on model performance.

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6. Approach 6: Training from Scratch

6.1 Definition

Training a Large Language Model (LLM) from scratch involves building a model entirely without using pre-trained components. This process includes selecting a model architecture, curating a diverse and extensive dataset, and conducting the training process. This approach is often resource-intensive and complex, requiring significant computational power and data-handling expertise.

6.2 Example of Illustration

Bloomberg's approach to creating a domain-specific LLM for financial technology is a notable example. They developed "BloombergGPT," a 50-billion-parameter model. The training involved a meticulously curated dataset comprising financial documents, news, filings, and general-purpose datasets. Despite large-scale training and data curation challenges, BloombergGPT excelled in financial domain tasks, demonstrating the potential of training from scratch for specialized applications.

6.3 Link to Read More 

The detailed study and methodology can be explored in the research paper "Training Large Language Models: A Deep Dive into BloombergGPT," which is available here.

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7. High-level relative complexity

In developing the Large Language Model (LLM), the complexity of approaches ranges widely. At one end of the spectrum, prompting is the most accessible technique, offering a user-friendly gateway to harnessing LLMs. As we move towards the other end, each successive method — from leveraging functions and retrieval-augmented generation to sophisticated strategies like knowledge distillation and fine-tuning — demands incrementally greater technical expertise and computational resources. The most complex of these methods is training a model from scratch, requiring substantial data, infrastructure, and domain knowledge.

Moreover, the easier the approach used the less chance of closing the limitations that a LLM is facing. For instance, prompting an LLM that was never trained in financial data would undoubtedly lead to unreliable results and hallucinations. In other words, the specific needed approach would depend on the degree of customization targeted by the end user.

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8. Conclusion

In conclusion, we've outlined the top six approaches to mitigate the limitations of Large Language Models, ranging from the relatively straightforward prompting to the more intricate process of training from scratch. Each method offers unique advantages and complexities tailored to enhance LLMs' performance in various scenarios.

As we explore each approach in our upcoming series of blogs, we aim to provide comprehensive insights, practical examples, and advanced strategies for fully leveraging the capabilities of LLMs across different industries and applications. 

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9. References

1. OpenAI. (2023). Prompt Engineering Guide. Retrieved from OpenAI Documentation.
2. Packer, C., Fang, V., et al. (2023). Towards LLMs as Operating Systems. UC Berkeley. Retrieved from MemGPT Research
3. Lewis, P., Perez, E., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In NeurIPS 2020. Retrieved from NeurIPS Proceedings.
4. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network. Retrieved from arXiv.
5. Gunasekar, S., Zhang, Y., et al. (2023). Textbooks Are All You Need. Retrieved from Microsoft Research.
6. Dodge, J., Ilharco, G., et al. (2020). Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping. Retrieved from arXiv.
7. Wu, S., Irsoy, O., et al. (2023). BloombergGPT: A Large Language Model for Finance. Retrieved from arXiv.
8. Bloomberg Professional Services. (2023). Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance. Retrieved from Bloomberg.

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