The 10 Best Large Language Models to Watch in 2023

The field of large language models (LLMs) is advancing at an incredible pace. In 2023, we have seen the release of unprecedentedly powerful models that represent major leaps in natural language processing capabilities.

we will explore the 10 Best Large Language Models that have taken the AI community by storm and fundamentally pushed boundaries in generative intelligence. For each model, we’ll highlight key capabilities, use cases, and considerations for tech enthusiasts and businesses exploring real-world applications.

1. GPT-4: The New Gold Standard

Released by OpenAI in March 2023, GPT-4 firmly establishes itself as the new undisputed leader in the LLM arena. With over 1 trillion parameters, it’s one of the largest models ever created. GPT-4 displays human-level proficiency across a multitude of NLP tasks and excels in text generation, translation, summarization, and question-answering.

Arguably GPT-4’s most groundbreaking capability is its aptitude for multimodal understanding, able to process images alongside text. This paves the way for a new generation of AI applications leveraging both visual and textual inputs.

For businesses, GPT-4 promises to turbocharge customer service, marketing content creation, and process automation. Its unmatched versatility makes it a valuable asset for enterprises seeking frictionless natural language interfaces.

Key Features of GPT-4:

• Cutting-edge natural language processing capabilities enable human-like comprehension and generation.
• Multimodal learning combines textual and visual data for enhanced reasoning.
• Massive neural network architecture and dataset training driving unparalleled performance.
• Rigorous accuracy validation through extensive textual and code training.
• Impressive linguistic fluency and creativity in text generation applications.

 

2. GPT-3.5: A Trusty All-Rounder

The 175 billion parameters GPT-3.5 established itself in 2022 as a robust general-purpose LLM. While less specialized than GPT-4, GPT-3.5 remains popular in 2023 for its speed, versatility, and accessibility.

GPT-3.5 handles text generation for essays, stories, and business plans adeptly. The newly released 16K token context window for GPT-3.5 Turbo allows it to better track long conversational threads. GPT-3.5 is also free to use without rate limits through some third-party portals.

Key Features of GPT-3.5:

• Swift processing speed, outpacing GPT-4.
• Proficient in creative applications like poetry, code generation, and more.
• Strong aptitude for addressing coding questions and challenges.
• Challenges with hallucinated content and false information.

3. PaLM: Google’s Impressive New Contender

PaLM 2, the formidable creation from Google AI, shines in commonsense reasoning and advanced coding. It has outperformed even GPT-4 in reasoning evaluations and can generate code in multiple languages.

Notably, PaLM 2 consistently outperforms GPT-4 in evaluations targeting deductive reasoning, common sense, and domain-specific knowledge. This evidences its deeper comprehension of logic and causal relationships.

Palm models also showcase rapid response times, providing multiple answer options simultaneously. The largest PaLM configuration forms the foundation for Google’s new experimental Bard search chatbot, providing public access to its capabilities.

Key Features of PaLM 2:

• Powerful commonsense reasoning abilities.
• Advanced coding skills across multiple programming languages.
• Multilingual competence in over 20 languages.
• Fast and highly responsive for efficient customer support.

4. Claude: Anthropic’s Assistant-Focused Model

Claude comes from artificial general intelligence startup Anthropic, founded by former OpenAI research leaders. True to its name, Claude aims specifically at AI assistant applications with an emphasis on helpfulness, honesty, and safety.

For businesses exploring AI chatbots and interactive agents, Claude’s combination of conversational proficiency and alignment with human values makes it a promising choice. Its robust training methodology minimizes unhelpful or unethical responses.

Key Features of Claude v1:

• Large 100k token context window for handling complex queries.
• Competitive performance alternative to GPT-4.
• Well-suited for AI assistants, research, and business uses.

5. Cohere: Enterprise-Ready Generative AI

Founded by former Google executives, startup Cohere focuses squarely on delivering easy-to-use large language models tailored to enterprise needs. Its Cohere Command model emphasizes accuracy and structure generation for production environments.

Cohere offers accurate and robust models tailored for enterprise generative AI. The Cohere Command model is notable for its accuracy, making it an excellent choice for businesses.

Notable customers using Cohere for AI applications include Spotify, Intuit, and Horizon Robotics. The combination of enterprise reliability, robust training, and pay-as-you-go pricing makes Cohere attractive for businesses exploring real-world generative AI tools.

Key Features of Cohere:

• Accurate and robust models trained on large text and code data.
• Cohere Command enterprise-focused generative AI model.
• Reliable generative AI solution for businesses.

How To Run Powerful LLMs Locally: A Simple Guide

6. Falcon: Cutting-Edge Open Source Model

Falcon, the first open-source large language model on our list, has outperformed all the open-source models released so far. Developed by the Technology Innovation Institute (TII), UAE, it offers unique features and capabilities. LLM. Available in 40 billion and 7 billion parameter configurations.

Falcon handles English, German, French, and Spanish adeptly in initial benchmarks. However, its open-source nature allows community development to potentially expand its multilingual capabilities and fine-tune Falcon for localized applications.

For startups and smaller players seeking access to generative AI without heavy licensing costs, Falcon provides a next-generation open-source model with immense potential for customization. Its availability also promotes transparency and trust for public sector use cases.

Key Features of Falcon:

• Apache 2.0 license enabling commercial use.
• Available in 40B and 7B parameter models.
• Fine-tuned for conversational and other uses.
• Multilingual capabilities.

7. LLaMA: Meta’s Open-Source Option

LLaMA is a series of large language models developed by Meta. They come in different sizes, from 13B to 65B parameters, and claim to outperform GPT-3.

The smaller 7 billion parameter LLaMA-13B model already claims to outdo OpenAI’s GPT-3 API. However, the 65 billion parameter LLaMA-65B likely offers the most competitive performance. LLaMA models have become widely adopted for academic research into language AI techniques and capabilities.

LLaMA strictly stays within the boundaries of its open-source license terms for community benefit. However, initiatives like Falcon and Cohere offer complementary open-access options for startups seeking production models.

Key Features of LLaMA:

• Various model sizes are available.
• Outperforms GPT-3 in some evaluations.
• Released for research purposes.

8. Guanaco: Efficient Open Source Model

The non-profit group Opai has released Guanaco, an increasingly popular open-source LLM derived from Meta’s LLaMA architecture. Its 65 billion parameter configuration matches LLaMA-65B’s size while demonstrating solid performance at significantly greater training efficiency.

In particular, Guanaco-65B manages to train using just a single GPU system, greatly expanding access for students, researchers, and startups. It also allows running entirely offline for local private use.

Key Features of Guanaco-65B:

• Surpasses ChatGPT performance.
• Trained efficiently on a single GPU.
• Available for offline use.

9. Vicuna: Accessible 33B Parameter Model

Vicuna, another open-source model derived from LLaMA, is a powerful tool with 33 billion parameters. Developed by startup LMSYS, Vicuna aims to make advanced generative AI accessible at manageable resource requirements.

LMSYS provides a free demo to interact with the Vicuna-33B model through its website. Benchmark results confirm Vicuna’s skills in areas like mathematical reasoning and open-domain conversation match or exceed commercial offerings with over 5x its parameters.

For students and developers seeking hands-on experience with capable yet accessible LLMs, Vicuna warrants a close look. Its open-source availability removes any barriers.

Key Features of Vicuna 33B:

• 33 billion parameters.
• Strong results on AI benchmark tests.
• Demo available on LMSYS website.

 

10. MPT: A New Compact Open Source Option

MPT-30B, developed by Mosaic ML, offers an 8K token context length and outperforms GPT-3 on benchmark tests. The MPT-30B model reaches parity with GPT-3 despite using just 30 billion parameters.

A key advantage of MPT is its support for an 8,000 token input context size even on GPUs, allowing complex conversational modeling. The team is expanding MPT’s capabilities through careful dataset curation and model optimization techniques accessible to the community.

For resource-constrained teams seeking lightweight yet high-performance LLMs for product integration, MPT models merit consideration. Their parameters may be modest for now, but their performance reaches far above their weight class.

Key Features of MPT-30B:

• 8K token context length.
• Outperforms GPT-3 in assessments.
• Can be run locally.

Evaluating Best Large Language Models Capabilities

With new LLMs continuing to emerge at a rapid rate, how can we make sense of their capabilities empirically? Several benchmark suites provide insight:

• HumanEval: Measures a broad set of natural language and reasoning skills.
• MMLU: Assesses factual knowledge plus reasoning abilities.
• MT-Bench: Tests translation quality between languages.
• TruthfulQA: Focuses on model accuracy and honesty.

Although benchmarks provide one perspective, real-world testing based on intended use cases is recommended to fully characterize strengths and weaknesses.

Key Considerations for Adopting LLMs

As companies explore leveraging these powerful models, some key factors to evaluate:

• Intended use cases – Match models against required capabilities and performance levels.
• Accessibility – Consider model availability, commercial licensing, and technical resources required.
• Accuracy – Assess suitability for uses requiring high precision and verification.
• Responsible AI – Examine training data and methodologies for alignment with ethics.
• Security – Establish controls to prevent misuse and data exposure.

Testing prospective models on prototypical tasks and data is advised. However, the rapid evolution of LLMs makes flexibility pivotal, as improved alternatives emerge continually.

Outlook for the Future

It’s an incredibly dynamic time in natural language AI! Looking forward, here are some exciting directions on the horizon:

• More multimodal capabilities bridging vision, speech, and language
• Improvements in reasoning, logic, and situational adaptability
• Specialization in domains like science, medicine, and engineering
• Further compression and efficiency gains through model distillation and pruning
• Increased access through open source, startup commercialization, and cloud deployment
• Focus on alignment, transparency, and responsible development practices

While today’s models set a high bar, they represent just the tip of the iceberg. Sustained progress in fundamental LLM architectures, training techniques, and computing infrastructure will unleash ever more powerful and beneficial language AI capabilities over time.

Final Thoughts

This guide provided an overview of the leading large language models bringing significant advancements to natural language processing in 2023. Key highlights include:

• GPT-4 asserts itself as the new benchmark for generative AI trained on an unprecedented 1 trillion-plus parameters.
• Palm emerging from Google showcasing exceptional reasoning abilities and coding competency.
• Anthropic, Cohere, and other startups commercializing accessible, enterprise-ready LLMs.
• Open-source models like Falcon democratize access to cutting-edge generative AI.
• Ongoing benchmarking and responsible testing are needed to guide LLM adoption.

Rapid iteration of models like these continues expanding the frontiers of what’s possible with natural language AI. Harnessing their capabilities promises to transform industries, but thoughtfully and ethically. Exciting times lie ahead at the intersection of language and machine learning!