Welcome to the world of Large Language Models (LLMs)!
The introduction of ChatGPT in November 2022 has sparked a renewed interest in large language models (LLMs). These models have already significantly impacted various industries by generating human-like text and providing solutions to multiple applications. However, their effectiveness is hindered by several concerns, including bias, inaccuracy, and toxicity. These concerns have raised ethical issues and limited their broader adoption.
To unlock the full potential of these models, researchers are exploring promising approaches, such as self-training, fact-checking, and introducing better prompt engineering and fine-tuning techniques, to mitigate these issues and develop more accurate and ethical models.
Large Language Models: An Overview
Large language models (LLMs) are foundational models that leverage deep learning techniques for natural language processing (NLP) and natural language generation (NLG) tasks. To help them understand the complexity and linkages of language, LLMs are pre-trained on vast amounts of data using techniques such as fine-tuning, in-context learning, and zero-/one-/few-shot learning.
Industry analysts predict that the NLP market will rapidly grow from $11 billion in 2020 to over $35 billion in 2026. However, it’s not just the market size that’s large; the size of LLM models and the number of parameters involved are also substantial. The figure below demonstrates how the size of LLM models has been exponentially increasing in recent years.
Learn About the Role of Generative Language Models in Software Engineering Here: How Generative Language Models are Redefining Software Engineering
List of Popular Large Language Models (LLMs)
It is worth mentioning some popular LLMs and describing their significance. This list is not exhaustive, and new LLMs are introduced daily. For example, Meta introduced LLaMa as its brand-new collection of LLMs with varying parameters.
- T5 (Text-to-Text Transfer Transformer): T5 is a pre-trained LLM that uses a transformer architecture to perform several natural language processing tasks. Unlike other LLMs, T5 can perform multiple tasks using a single model, using a text-to-text transfer approach that allows it to adapt to different tasks with minimal fine-tuning. Developed by Google in 2019, T5 has 11 billion parameters.
- GPT-3 (Generative Pre-Trained Transformer 3): Developed by OpenAI in 2020, GPT-3 is one of the largest and most advanced LLMs, with 175 billion parameters. GPT-3 can perform various natural language processing tasks, including summarization, question answering, language translation, and text completion.
- LaMDA (Language Model for Dialogue Applications): LaMDA was announced in July 2021 by Google. Like other LLMs, such as GPT-3 and BERT, LaMDA can learn text representations that can be used for various NLP tasks. However, LaMDA is unique in several ways. First, it has 1.6 trillion parameters, making it much larger than most other LLMs. Second, it uses a novel architecture called Switch Transformer, allowing the model to switch between various task-specific modules as required.
- BERT (Bidirectional Encoder Representations from Transformers): BERT was introduced by Google in 2018 and is a pre-trained LLM that uses a transformer architecture to learn text representations. BERT has achieved state-of-the-art performance on several NLP tasks, including question answering, text classification, and language inference. BERT has 340 million parameters.
- RoBERTa (Robustly Optimized BERT Approach): Developed by Facebook AI in 2019, RoBERTa is a pre-trained LLM based on the BERT architecture but fine-tuned on a more extensive and diverse dataset. RoBERTa has achieved state-of-the-art performance on several NLP tasks, including text classification, question answering, and language modeling. RoBERTa has 355 million parameters.
The image given below demonstrates the timeline of large language models:
Why Do We Need Multimodal Language Models
Text-only LLMs like GPT-3 and BERT have diverse applications, including writing articles, composing emails, and coding. However, these models have limitations due to their exclusive reliance on text.
Human intelligence encompasses more than just language; it involves unconscious perception and abilities shaped by experiences and understanding of how the world operates. Text-only LLMs struggle to incorporate common sense and world knowledge, leading to challenges in certain tasks. Expanding the training data helps to some extent, but knowledge gaps can still arise unexpectedly. Multimodal approaches offer solutions to overcome these limitations.
For example, let’s consider ChatGPT and ChatGPT-4.
Even though ChatGPT is an impressive language model with widespread usefulness, it faces limitations in complex reasoning.
ChatGPT-4, the next iteration, aims to surpass ChatGPT’s reasoning capabilities. By employing advanced algorithms and integrating multimodality, ChatGPT-4 is poised to take natural language processing to the next level. It tackles complex reasoning problems and enhances its ability to generate human-like responses.
Bonus Read: ChatGPT-4 vs ChatGPT-3: Everything You Need to Know
PaLM-E is another example of a multimodal language model developed by researchers at Google and TU Berlin that revolutionizes robot learning by utilizing knowledge transfer across visual and language domains.
Unlike past efforts, PaLM-E directly integrates raw sensor data from the robotic agent during training. This results in a compelling robot learning model, making it ideal for general-purpose visual-language tasks.
What’s Next in Large Language Models (LLMs)?
As technology continues to evolve, promising developments are being made in the field of large language models (LLMs) that address some of the common issues these models face. In particular, there are three significant changes that researchers are focusing on for the future of language models.
1. Future Large Language Models Can Fact-Check Themselves
The first change involves improving the factual accuracy and reliability of LLMs by giving them the ability to fact-check themselves. This would allow the models to access external sources and provide citations and references for their answers, which is essential for real-world implementation.
Two promising models developed in this area are Google’s REALM and Facebook’s RAG, both introduced in 2020.
More recently, in June 2022, OpenAI introduced a fine-tuned version of its GPT model called WebGPT, which uses Microsoft Bing to browse the internet and generate more precise and comprehensive answers to prompts. The model works similarly to a human user, submitting search queries to Bing, clicking on links, browsing web pages, and deploying functions such as CTRL+F to locate relevant information.
When the model covers information from the internet in its output, it includes citations, enabling individuals to authenticate the source of information. Initial research results on WebGPT are promising, with the model outperforming all GPT-3 models in terms of the percentage of accurate responses and the amount of truthful and informative answers provided.
While it is impossible to predict precisely how LLMs will evolve in the future, these advancements offer hope that these models’ factual reliability and static knowledge limitations can be addressed. These changes will help prepare LLMs for broader real-world implementation, making them more effective and useful natural language processing and generation tools.
This figure shows TruthfulQA results comparing GPT-3 and WebGPT models:
Google DeepMind is also delving into similar research areas and has recently introduced a new language model named Sparrow. Like ChatGPT and WebGPT, Sparrow also functions on a dialogue-based approach and can search the internet for additional information while providing references to back up its findings.
While it’s too early to determine if upcoming models can overcome problems such as accuracy, fact-checking, and a static knowledge base, recent research indicates that the future may hold great promise. This may reduce the need for prompt engineering to verify the model’s output, as the model itself will have already double-checked its results.
2. LLMs Will Still Require Better Prompt Engineering Approaches
Although language models have shown impressive performance in various tasks, they still lack a complete understanding of language and the world, unlike humans. This can result in unexpected behavior and mistakes that may appear mindless to users.
To address this issue, prompt engineering techniques have been developed to guide LLMs to produce more accurate output. Few-shot learning is one such method, where prompts are created by adding a few similar examples and the desired outcome, which serve as guides for the model to produce its output. By creating datasets of few-shot examples, the performance of LLMs can be improved without retraining or fine-tuning them.
Chain-of-Thought (COT) prompting is another exciting series of techniques that enables the model to produce an answer and the steps it uses to reach that answer. This technique is beneficial for tasks requiring logical reasoning or step-by-step computation.
Reasoning and logic pose fundamental challenges to deep learning that require new architectures and AI/ ML approaches. But for now, prompt engineering techniques can help reduce the logical errors made by LLMs and facilitate troubleshooting mistakes.
3. Better Fine-Tuning & Alignment Approaches
Customization is vital for LLMs; fine-tuning them with application-specific datasets can significantly enhance their performance and robustness. This is especially true for specialized domains where a general-purpose LLM cannot provide accurate results.
In addition to traditional fine-tuning techniques, new approaches are emerging that can further improve the accuracy of LLMs. One such strategy, called “reinforcement learning from human feedback” (RLHF), was used to train ChatGPT.
With RLHF, human annotators provide feedback on the LLM’s answers, which is then used to train a reward system that fine-tunes the model and aligns it better with user intents. This method has proven highly effective and is a key reason ChatGPT-4 outperforms its predecessors in following user instructions.
Right now, there is a competitive race to develop larger language models.
Whether it’s the Israeli Jurassic-1 with 178 billion parameters, the Chinese Wu Dao with 1.75 trillion parameters, or ChatGPT-4 with 175 trillion parameters, building these massive models is no longer exclusive to companies like Google or Microsoft. Innovation in this field is becoming more widespread and diverse.
Moving forward, LLM providers must develop tools that allow companies to create their own RLHF pipelines and customize LLMs for their specific applications. This will be a crucial step in making LLMs more accessible and useful for a broader range of industries and use cases.
Graphcore CTO Simon Knowles said: “It’s quite clear that an AI doesn’t have to tap into all of its knowledge to accomplish a specific task if it’s capable of performing multiple functions. This is analogous to how the human brain functions, and it should be the standard approach for developing artificial intelligence. I wouldn’t be shocked if, within the next year, there’s a significant reduction in the number of dense language models being developed.”
As the field of natural language processing (NLP) continues to advance, it is exciting to see how new developments will address the remaining challenges that LLMs face. While we have seen promising progress in areas such as fact-checking, fine-tuning, and prompting techniques, much work remains to be done.
As LLMs become more reliable, they will undoubtedly become more accessible to developers and researchers. This could lead to new applications and use cases that were previously out of reach, as well as advancements in areas such as machine translation, speech recognition, and text generation.
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As we continue to develop and refine these models, it will be fascinating to see how they evolve and what new capabilities they will enable.
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