Benefits, Threats and Types of LLMs

Large language models (LLMs) serve as foundation models that generate text, translate between languages, and write various forms of material using artificial intelligence (AI), deep learning, and vast data sets, including websites, articles, and books. These generative AI models come in two varieties: closed-source big language models and open-source large language models.

A corporation owns proprietary LLMs, which only licensed consumers can use. The license may limit LLM use. However, open source LLMs are free to use, change, and distribute.

The LLM code and architecture are “open source” so developers and academics can use, improve, and adapt the model.

Benefits of open-source LLMs?

Larger LLMs were always considered superior, but today companies realize they might be prohibitively expensive for research and innovation. A promising open source model ecosystem challenged the LLM business model.

Being transparent and flexible

Open source LLMs offer transparency and flexibility for companies without in-house machine learning talent, whether in the cloud or on premises. They have full control over their data and sensitive information stays on their network. All this decreases data leak and unauthorized access risks.

Open source LLMs disclose their design, training data, techniques, and use. Code inspection and algorithm visibility boost trust, aid audits, and ensure ethical and legal compliance. Effectively tuning an open source LLM reduces latency and boosts performance.

Cost-saving

They cost less than proprietary LLMs over time since there are no licensing fees. LLMs require cloud or on-premises infrastructure and a large initial installation cost.

New features and community contributions

Adjustments are possible with open-source, pre-trained LLMs. Enterprises can train LLMs on specific datasets and add features for their use. Working with a vendor to update or specify a proprietary LLM takes time and money.

While proprietary LLMs require a single provider, open source ones allow a company to use community contributions, numerous service providers, and perhaps internal teams for updates, development, maintenance, and support. Open source lets companies try new things and use diverse contributions. That can lead to cutting-edge technology solutions for businesses. Businesses employing open source LLMs have more flexibility over their technology and usage options.

What projects may open-source LLM models enable?

Open source LLM models can be used to construct almost any project for employees or, if the license allows, commercial products. This includes:

Text creation

Language generation apps like emails, blog articles, and creative stories can be created using open source LLM models. Falcon-40B, an Apache 2.0 LLM, can respond to a prompt with high-quality text suggestions you may tweak and improve.

Code creation

Developers can use open source LLMs trained on existing code and programming languages to construct applications and discover security issues.

Virtual instructing

Open source LLMs allow you to construct individualized learning apps that may be tailored to specific learning styles.

Content summary

An open-source LLM tool that summarizes big articles, news pieces, research reports, and more to simplify data extraction.

Chatbots powered by AI

These understand and answer questions, make comments, and talk naturally.

Translating languages

Open source LLMs trained on multilingual datasets can translate numerous languages accurately and fluently.

Analysis of sentiment

LLMs can assess text’s emotional tone for brand reputation management and consumer feedback analysis.

Moderate and filter content

LLMs can recognize and filter hazardous online information, making the internet safer.

Which organizations employ open-source LLMs?

Many organizations employ open-source LLMs. IBM and NASA created an open-source LLM trained on geographical data to help scientists and organizations battle climate change.

Publishers and journalists use open-source LLMs to analyze, identify, and summarize data without sharing proprietary data.

Some healthcare institutions employ open source LLMs for diagnosis, treatment optimization, patient information, public health, and other software.

The open-source LLM FinGPT was created for finance.

Top open-source, curated LLMs

Open LLM Leaderboard tracks, ranks, and evaluates open source LLMs and chatbots on benchmarks.

• The Watsonx.ai studio offers Meta AI’s LLaMa 2, a well-performing open source LLM with a commercial license. It includes pre-trained and fine-tuned generative text models with 7 to 70 billion parameters. Hugging Face ecosystem and transformer library offer it.
• Vicuna and Alpaca, like Google’s Bard and OpenAI’s ChatGPT, are LLaMa-based and trained to follow commands. Vicuna matches GPT-4, outperforming Alpaca.
• Over 1,000 AI researchers built Bloom by BigScience, a multilingual language model. This is the first transparently trained multilingual LLM.
• The Falcon LLM from Technology Innovation Institute (TII) can help chatbots write innovative text, tackle complex challenges, and perform monotonous chores. Falcon 6B and 40B are provided as raw models for fine-tuning or instruction-tuned models for use. Falcon greatly beats GPT-3 with only 75% of its training compute resource.
• MosaicML, recently acquired by Databricks, licenses open source LLMs MPT-7B and MPT-30B for commercial use. LlaMA and MPT-7B perform similarly. MPT-30B beats GPT-3. These are 1T token-trained.
• Over 1,800 tasks may be done by Google AI’s FLAN-T5.
• StarCoder from Hugging Face is an open-source LLM coding assistance educated on GitHub permissive code.
• Along with leaders from the University of Montreal and Stanford Center for Research on Foundation Models, Together developed RedPajama-INCITE, a 6.9B parameter pre-trained language model licensed under Apache-2.
• Cerebras-GPT has seven GPT models with 111 million to 13 billion parameters.
• Stability AI, which created Stable Diffusion, created StableLM, an open-source LLM. It trained on “The Pile” dataset of 1.5 trillion tokens and fine-tuned with open source datasets from Alpaca, GPT4All (which offers models based on GPT-J, MPT, and LlaMa), Dolly, ShareGPT, and HH.

Risks of Large Language Models

LLM outputs sound fluent and authoritative, but they may contain “hallucinations” and bias, consent, or security issues. Data and AI challenges can be addressed by risk education.

• LLMs trained on partial, conflicting, or erroneous data or that predict the next accurate word based on context without understanding meaning might produce hallucinations.
• Data bias occurs when sources are neither varied or representative.
• Consent relates to whether the training data was acquired accountablely, using AI governance mechanisms that comply with laws and regulations and allow for input.
• Leaking PII, cybercriminals utilizing the LLM for phishing and spamming, and hackers modifying original programming are security issues.

Open-source and IBM AI models, especially LLMs, will be transformational technologies in the coming decade. The data placed into AI must be managed and governed as new AI legislation impose guidelines on its use.

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