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How to Overcome the Limitations of Large Language Models

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Introduction

Large Language Models (LLMs) are at the forefront of the AI revolution, transforming how we interact with technology and the world around us. These deep learning algorithms, trained on massive datasets, are capable of recognizing, summarizing, translating, predicting, and generating text and other forms of content. From natural language processing applications like translation, chatbots, and AI assistants to more specialized uses in healthcare, software development, and beyond, LLMs are becoming increasingly integral to our digital lives.

However, as with any technology, LLMs come with their own set of limitations. Understanding limitations is crucial for the continued development and refinement of these models, ensuring they can be used safely and effectively. This blog post will delve into the limitations of LLMs, compare them with Foundation Models, and explore strategies for overcoming the limitations.

Understanding LLM Limitations

LLMs, despite their impressive capabilities, are not without their flaws. These limitations can range from issues with understanding context to generating misinformation and ethical concerns. The four limitations of Large Language Models (LLMs) discussed in this blog – contextual understanding, generating misinformation, ethical concerns, and lack of creativity – were chosen due to their significant impact on the performance and application of these models. They represent fundamental challenges in natural language processing and machine learning, and addressing them is crucial for the safe and effective use of LLMs. These limitations also reflect broader concerns in the field of AI, including the spread of misinformation, ethical implications, and the quest for genuine creativity. Let’s delve into some of the limitations in detail:

  • Contextual Understanding: While LLMs are trained on vast amounts of data and can generate human-like text, they sometimes struggle with understanding context. For example, they might not differentiate the two meanings of the word “bark” based on its context. E.g.
  • “The dog’s bark echoed through the quiet street.” In this sentence, “bark” refers to the sound a dog makes.
  • “The child scraped his knee on the rough bark of the tree.” Here, “bark” refers to the outer covering of a tree.

An LLM might struggle to differentiate between these two meanings of “bark” based on context, which could lead to incorrect or nonsensical responses. For instance, if asked to continue the story after the second sentence, an LLM might incorrectly assume that “bark” refers to a dog’s sound, leading to a response that doesn’t make sense in the given context.

  • Generating Misinformation: LLMs can sometimes generate content that is factually incorrect or misleading. This is because they generate responses based on patterns learned from their training data, which may include incorrect or misleading information.
  • Ethical Concerns: There are also ethical concerns related to the use of LLMs. For instance, they can be used to generate deep fake text or to automate the creation of misleading news articles or propaganda.
  • Lack of Creativity: While LLMs can generate text that seems creative, it’s important to remember that they are essentially pattern recognition systems. They do not truly understand or create new content in the same way a human would. Their “creativity” is based on mimicking patterns in the data they’ve been trained on.

In the following sections, we will delve deeper into these limitations, compare LLMs with Foundation Models, and explore potential strategies for overcoming such limitations. The Diagram below gives a good overall representation of the hierarchy, as we will discuss in the content below.

Understanding LLM Limitations

Foundation Model vs. LLM: A Comparative Analysis

Foundation Models and LLMs are both powerful tools in the field of AI. The comparison between Foundation Models and LLMs is not just an academic exercise. It serves a practical purpose in our quest to overcome the limitations of LLMs. Each of these model types represents a different approach to handling the complexities of language understanding and generation, and each has its strengths and weaknesses.

Through their emphasis on fine-tuning, Foundation Models offer a potential solution to some of the limitations of LLMs. For instance, the fine-tuning process can help mitigate issues such as the generation of misinformation or harmful content, which are significant concerns with LLMs. By studying how Foundation Models are designed and how they operate, we can glean insights into how to improve the safety and reliability of LLMs

Here’s a comparison of these two types of models:

Definition and Explanation

Foundation Models and Large Language Models (LLMs) both represent significant advancements in the field of Artificial Intelligence (AI), with each offering unique capabilities and characteristics.

Foundation Models are a class of AI models pre-trained on a broad range of internet text. They are designed to understand and generate human-like text, much like LLMs. However, what sets Foundation Models apart is their ability to be fine-tuned for specific tasks and applications. This fine-tuning process allows Foundation Models to adapt to a wide variety of tasks, from text classification and sentiment analysis to question answering and summarization. For more information on Foundation Models, you can visit this link.

On the other hand, Large Language Models (LLMs) like GPT-3 and BERT are also trained on vast amounts of text data and can generate creative, human-like text. They excel in tasks that involve generating long, coherent pieces of text and can be used in a wide range of applications, from chatbots and virtual assistants to content creation and programming help. However, unlike Foundation Models, LLMs are not specifically designed for fine-tuning, which can make them less adaptable to specific tasks. You can learn more about LLMs here.

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Comparison

For instance, while LLMs excel at generating human-like text, they can sometimes produce outputs that are nonsensical or even harmful. On the other hand, Foundation Models, while also capable of generating high-quality text, are designed to be more controllable and adaptable to specific tasks.

Quality of Generated Text:
LLMs, especially those with many parameters like GPT-3, are known for their ability to generate impressively human-like text. They can write essays, create poetry, and even generate code. However, the quality of their output can sometimes be inconsistent. They can produce outputs that are nonsensical or even harmful, especially when they are given ambiguous or misleading prompts.

On the other hand, Foundation Models, while also capable of generating high-quality text, are designed to be more controllable. They can be fine-tuned on specific tasks, which can lead to more reliable and task-specific outputs. For instance, a Foundation Model fine-tuned on medical text could be more reliable in generating medical advice than a general-purpose LLM.

Adaptability:
LLMs are general-purpose models. They are trained on a diverse range of internet text and can handle a wide variety of tasks. However, they are not specifically designed for fine-tuning. This means that while they can be adapted to new tasks, the process can be complex, and the results may not always be optimal.

Foundation Models, in contrast, are designed with adaptability in mind. They are pre-trained on a broad corpus of text, much like LLMs, but they are designed to be fine-tuned on specific tasks. This makes them more adaptable to new tasks and applications.

Controllability:
One of the challenges with LLMs is controlling their output. They can sometimes generate content that is inappropriate, biased, or factually incorrect. Efforts are being made to improve the controllability of LLMs, but it remains a significant challenge.

Foundation Models offer more controllability. Because they can be fine-tuned on specific tasks, it’s easier to control their outputs. For instance, you could fine-tune a Foundation Model on a dataset of polite and respectful text to create a model that generates more polite responses.

In conclusion, while both LLMs and Foundation Models have their strengths and weaknesses, they complement each other in various ways. LLMs’ ability to generate creative, human-like text can be leveraged for tasks that require a high degree of creativity, while Foundation Models’ controllability and adaptability make them suitable for tasks that require more precision and reliability.

Overcoming the Limitations of Large Language Models

Now that we understand the importance and concept of LLMs better, let’s talk about some of the fixes for their limitations. There are strategies and techniques that can be used to overcome these challenges:

Evaluation methods:

It’s crucial to understand the limitations of LLMs, such as their potential to generate nonsensical or harmful content. Rigorous evaluation methods should be used to address these limitations in specific use cases. Some of the possible evaluation methods can be:

  • Human Evaluation: This involves having human evaluators review and rate the model’s outputs. This can help identify instances where the model generates nonsensical or harmful content. It’s particularly useful for evaluating the model’s performance on tasks that require a high level of understanding or creativity.
  • Automated Metrics: These are quantitative measures that can be calculated automatically, such as BLEU for translation tasks or ROUGE for summarization tasks. While these metrics have limitations, they can provide a quick and scalable way to evaluate a model’s performance.
  • Adversarial Testing: This involves trying to “break” the model by giving it difficult or misleading inputs. This can help identify weaknesses in the model’s understanding and generation capabilities.
  • Fairness and Bias Evaluation: This involves testing the model’s outputs for biases, such as gender or racial bias. Various tools and metrics are available for this, such as the AI Fairness 360 toolkit from IBM.
  • Out-of-Distribution Testing: This involves testing the model on data that is different from the data it was trained on. This can help identify how well the model generalizes to new types of inputs.

Managing Token Limits and Memory

Tokens are the building blocks of text in LLMs, and token limits are implemented to ensure efficient performance. This ensures that we are not overextending the model and the infrastructure resources supporting it to provide timely responses to all users for their API requests. Understanding and managing these token limits can help maintain the context and ensure a smooth dialogue. The number of tokens for various models for openAI is visible in the following figure.

Managing Token Limits and Memory

Designing Effective Prompts

The design of prompts can significantly influence the outputs of LLMs. Effective, prompt design can help obtain more accurate and useful outputs from the models. Some of the techniques to do the same are Prompt Engineering, Prompt-Based Learning, Prompt-Based Fine-Tuning, Prompt Tuning

Mitigating Bias

LLMs learn from the data they are trained on, which can often include biased information. To mitigate this, researchers are exploring techniques like differential privacy and fairness-aware machine learning. A paper titled “Fairness and Abstraction in Sociotechnical Systems” provides a comprehensive overview of these techniques. Bias mitigation has in itself many further components.

Improving Transparency

One of the challenges with Large Language Models is understanding why they make certain predictions. However, there are practical tools and techniques available that can help make these models more interpretable.

For instance, Explainable AI (XAI) is a field of AI that focuses on creating techniques and models that make the decision-making process of AI systems clear and understandable to humans. There are several open-source libraries available that implement XAI techniques, such as LIME and SHAP. These libraries provide tools that can help you understand and visualize the decision-making process of your models.

In addition, there are also online platforms like IBM’s AI Explainability 360, which provides interactive demos and helpful resources to understand the concepts of explainable AI.

Controlling Output

Controlling the output of LLMs is crucial to ensure they generate safe and useful content. Techniques like reinforcement learning from human feedback (RLHF) are being used to achieve this. The paper “Fine-Tuning Large Language Models with Human Feedback” provides a deep dive into this technique.

Case Studies of Successful Limitation Handling

OpenAI’s ChatGPT

OpenAI has been actively working on reducing harmful and untruthful outputs from ChatGPT.To address this, OpenAI has been using a technique known as Reinforcement Learning from Human Feedback (RLHF). This involves collecting feedback from human evaluators on the model’s outputs and then using this feedback to train the model to generate better responses. This iterative process helps to reduce the likelihood of the model generating harmful or misleading content. They have also started a research project to make the model customizable by individual users, within broad bounds.

Google’s BERT

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based machine learning technique for natural language processing developed by Google. It’s designed to understand the context of words in a sentence and has been a game-changer in tasks like answering questions, understanding language, and sentiment analysis.

However, understanding why BERT makes certain predictions can be challenging. To address this, Google has been using techniques like attention visualization. Attention in transformer models like BERT is a mechanism that decides where to focus when processing input data. Visualization of this attention allows users to see which parts of the input the model focuses on when making predictions. This can provide insights into why the model is making certain decisions and can help improve the transparency and interpretability of the model.

IBM’s Project Debater

IBM has successfully used techniques like argument mining. Argument mining is a subfield of natural language processing (NLP) that automatically extracts and identifies argumentative structures from text. These structures can include claims, evidence, counterarguments, and other components of an argument. To control the output of Project Debater, a system that can debate humans on complex topics. This has allowed them to ensure the system generates relevant and coherent arguments.

Examples of Architectural Changes Leading to Improvements

BERTweet

BERTweet is a large-scale pre-trained language model specifically designed for English Tweets. It shares BERT-base’s architecture and is trained using the RoBERTa pre-training procedure. This architectural choice allows BERTweet to outperform previous models, setting new performance benchmarks on three Tweet NLP tasks. You can learn more about BERTweet here.

coCondenser

coCondenser introduces an innovative approach to Dense Passage Retrieval by adding an unsupervised corpus-level contrastive loss to warm up the passage embedding space. This architectural change eliminates the need for heavy data engineering and large batch training, making the model more efficient and effective. More details about coCondenser can be found here.

PolyCoder

PolyCoder is a new model based on the GPT-2 architecture, trained on a vast amount of code across 12 programming languages. With its 2.7B parameters, PolyCoder represents a significant architectural advancement in the field of code language models, outperforming all models, including Codex, in tasks involving the C programming language. You can read more about PolyCoder here.

Knowledge Graph-Based Synthetic Corpus Generation

This approach involves verbalizing a comprehensive Knowledge Graph (KG) like Wikidata, converting it into natural text that can be integrated into existing language models. The architectural innovation here lies in the seamless integration of structured KG data with language models, improving factual accuracy and reducing toxicity. More information about this approach can be found here.

Conclusion

LLMs have undeniably transformed the landscape of NLP, offering unprecedented capabilities in understanding and generating human-like text. However, as we’ve explored in this blog, they are not without limitations. Issues such as model bias, lack of transparency, and difficulty in controlling the output are significant challenges that need to be addressed.

Fortunately, the research community is working on strategies and techniques to overcome these limitations. From mitigating bias through adversarial text prompts and end-to-end bias mitigation techniques to improving transparency with Explainable AI and controlling output with reinforcement learning from human feedback, numerous promising approaches are being explored.

Moreover, LLM architecture itself provides a key to overcoming these limitations. By understanding and modifying this architecture, we can make strides in overcoming these limitations. Examples of such modifications, like BERTweet, coCondenser, PolyCoder, and the verbalization of comprehensive Knowledge Graphs, have shown significant improvements in model performance.

In conclusion, while LLMs have their limitations, the future is promising. The key is to continue research and development in these areas to make these models more reliable, transparent, and useful. As we continue to refine these models and develop new strategies to overcome their limitations, the potential of LLMs in a wide range of applications becomes even more exciting. The journey of LLMs is just beginning, and the road ahead is full of possibilities.

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