The future of AI isn’t locked behind proprietary paywalls—it’s open and collaborative, with open source LLMs giving businesses the power to innovate on their own terms.
Artificial intelligence is no longer confined to the tech companies with deep pockets. With the advent of generative AI, large language models (LLMs) are being developed and used across a wide range of domains. Open source LLMs are also growing in popularity, which means businesses of all sizes can now leverage advanced AI without the hefty price tags or restrictive terms of proprietary models.
With LLM options like Falcon, Llama 3, BLOOM, and BERT, AI is becoming more accessible across industries, helping automate processes, enhance customer experiences, and improve decision-making. However, there are still some concerns like security and AI hallucinations when using these open source LLMs.
How open source LLMs are powering different industries
The beauty of open source LLMs lies in their versatility. Businesses across different sectors are tapping into their power to solve unique challenges. Here are just a few ways they’re being used.
Finance and banking
- Spotting fraud in real time: AI models scan transactions for suspicious patterns, helping detect fraud before it happens.
- Smarter investment advice: Automated wealth management tools analyse financial trends and offer personalised strategies.
- Risk assessment made easy: By crunching massive datasets, AI helps predict market trends and assess credit risks more accurately.
Healthcare and pharma
- Faster medical documentation: Doctors can dictate notes while AI transcribes and summarises them instantly.
- Accelerating drug discovery: LLMs sift through scientific papers to identify potential breakthroughs.
- AI-powered patient support: Chatbots handle common medical questions, improving access to healthcare information.
Retail and e-commerce
- Personalised shopping experiences: AI analyses customer behaviour to suggest products they’ll love.
- Instant customer support: AI chatbots reduce wait times and handle common questions efficiently.
- Better inventory management: AI-driven demand forecasting helps retailers avoid stock shortages and excess inventory.
Manufacturing and supply chains
- Preventing machine failures: Predictive maintenance keeps equipment running smoothly by identifying issues before they cause downtime.
- Optimising logistics: AI finds the most efficient delivery routes, reducing costs and delays.
- Improving quality control: AI-powered inspection systems catch defects faster and more accurately than humans.
Education and research
- AI tutors that adapt to students: Personalised learning tools adjust to each student’s progress.
- Making academic research easier: AI speeds up literature reviews by summarising key insights from thousands of papers.
- Breaking language barriers: AI-powered translation tools make learning materials accessible to a global audience.
From finance to education, open source LLMs are proving to be game-changers—offering customised, scalable solutions without the constraints of proprietary systems.
Security in open source LLMs
Security is always a major concern when implementing AI, and open source models come with their own set of advantages and challenges. The good news? They offer far more transparency and control than closed source alternatives.
Full transparency and security audits
Unlike proprietary models, open source LLMs allow developers to inspect every line of code and review training data. This means security flaws can be spotted and fixed faster by the global developer community.
On-premise deployment for maximum privacy
Companies worried about data security can choose to host these models on their own infrastructure, keeping sensitive information completely in-house. This eliminates risks tied to third-party cloud providers.
Customisable security layers
Because the code is open, businesses can add their own encryption, authentication, and access controls, ensuring their AI system meets specific security requirements.
A global community for faster fixes
Unlike closed models where users depend on a single company for updates, open source AI benefits from a worldwide network of developers who actively monitor and improve security.
So, while security challenges exist, open source models provide something invaluable—the ability to take control rather than relying on a black-box solution. Until recently, training and fine-tuning LLMs required enormous computational resources, limiting AI development to well-funded organisations. Open source LLMs are breaking down these barriers by allowing anyone to experiment with and deploy AI models tailored to their needs.
For example:
Falcon delivers state-of-the-art performance with efficiency, making it a great option for enterprises looking for cost-effective AI.
Llama 3 (Large Language Model Meta AI 3), Meta’s latest release, balances power and accessibility, enabling developers to integrate advanced NLP into their applications.
BLOOM (BigScience Large Open-science Open-access Multilingual Language Model), created by Hugging Face, is a multilingual powerhouse designed to support diverse global applications. It has 176 billion parameters for building transformer-based autoregressive LLMs.
BERT (Bidirectional Encoder Representations from Transformers) from Google, one of the earliest and most influential open source NLP models, continues to be widely used for search, chatbots, and more.
These models are not just alternatives to proprietary solutions—they’re often just as capable, if not better in some cases, depending on the use case.
Table 1: Comparison of the features of different open source LLMs
| Model | Developer | Strengths | Best use cases | Licence |
| Falcon | TII | High efficiency, strong performance | Enterprise applications, chatbots | Apache 2.0 |
| Llama 3 | Meta | Powerful, optimised for fine-tuning | AI assistants, research, apps | Custom (free for research and commercial use with conditions) |
| BLOOM | Hugging Face | Multilingual, diverse dataset | Global applications, translation | Apache 2.0 |
| Mistral 7B | Mistral AI | Compact yet powerful, strong reasoning | Lightweight AI applications, code generation | Apache 2.0 |
| BERT | Optimised for NLP tasks, widely used | Search engines, sentiment analysis | Apache 2.0 |
How open source LLMs handle AI hallucinations
AI hallucination refers to instances where a language model generates content that is factually incorrect, logically inconsistent, or entirely fabricated. While hallucination is a known issue across all LLMs, handling it in open source models presents unique challenges and opportunities due to the collaborative nature of their development. Below is an in-depth explanation of how hallucination is handled in open source LLMs, broken down into key steps and strategies.
1. Dataset curation and preprocessing
Root cause
Hallucinations often stem from noisy, biased, or unverified data in the training corpus.
Solution
- Open source communities emphasise high-quality dataset curation. They actively filter out unreliable or low-quality data using automated tools and human-in-the-loop systems.
- Preprocessing pipelines include:
- Deduplication of text to avoid overfitting on repetitive or redundant information.
- Source verification to include only credible and authoritative data sources.
- Bias mitigation by balancing datasets to reduce over-representation of specific viewpoints.
Example
The OpenAI GPT-3 dataset was heavily curated to remove low-quality web content, and similar practices are followed by open source projects like Hugging Face’s datasets.
2. Reinforcement Learning from Human Feedback (RLHF)
Root cause
LLMs lack an inherent understanding of factual correctness, as they are trained to predict the next token based on probability.
Solution
- Open source LLMs adopt RLHF to align model outputs with human expectations of truthfulness and coherence.
- Steps in RLHF:
a. Human annotation: Annotators rank the outputs of the model based on factual accuracy and relevance.
b. Reward modelling: A reward model is trained to predict these rankings.
c. Policy optimisation: The base LLM is fine-tuned using reinforcement learning to maximise the reward signal.
- Community contributions: Open source projects often crowdsource human feedback for RLHF, enabling a diverse range of perspectives and higher-quality annotations.
Example
OpenAssistant, an open source alternative to ChatGPT, uses RLHF extensively to improve response reliability.
3. Instruction-tuning and fine-tuning
Root cause
LLMs trained on general-purpose data may lack domain-specific knowledge, leading to hallucination when queried on specialised topics.
Solution
- Instruction-tuning: Models are fine-tuned on datasets containing high-quality question-answer pairs or task-specific instructions.
- Domain-specific fine-tuning: For niche use cases, open source LLMs are fine-tuned on curated datasets from specific domains (e.g., medical, legal, or technical).
- Community-driven datasets: Open source contributors create and share fine-tuning datasets, ensuring broader coverage and higher reliability.
Example
Llama and its derivatives like Vicuna are fine-tuned using instruction datasets like ‘Alpaca’ to reduce hallucination.
4. Fact-checking and Retrieval-Augmented Generation (RAG)
Root cause
LLMs lack real-time access to updated or external factual information.
Solution
- RAG framework:
- Open source LLMs integrate with external knowledge bases (e.g., Wikipedia, scientific databases) during inference.
- Instead of generating answers purely from internal weights, the model retrieves relevant documents and uses them to ground its responses.
- Automated fact-checking:
- Outputs are cross-referenced with trusted sources using automated scripts or APIs.
- Community members often build plugins for open source LLMs to integrate real-time fact-checking tools.
Example
LangChain, an open source framework, enables LLMs to use tools like search engines or APIs for real-time retrieval, reducing hallucination.
5. Model architecture improvements
Root cause
The architecture of LLMs may inherently contribute to hallucination due to over-reliance on statistical patterns.
Solution
- Smaller, specialised models: Open source projects often train smaller, domain-specific models that are less prone to hallucination compared to general-purpose LLMs.
- Attention mechanisms: Enhanced attention mechanisms are used to better capture long-range dependencies and contextual relevance.
- Prompt engineering: Techniques like chain-of-thought prompting and self-consistency decoding are employed to guide the model towards more logical and factually accurate outputs.
Example
Research on sparse attention mechanisms in open source models like BigScience’s BLOOM has shown promise in reducing hallucination.
6. Community audits and iterative refinement
Root cause
Open source LLMs are dynamic, and new issues may arise as models evolve.
Solution
- Collaborative audits: Open source communities actively audit model outputs, identifying patterns of hallucination and sharing findings.
- Bug reporting and patches: Contributors can submit bug reports and propose patches to address specific hallucination issues.
- Iterative training: Models are retrained or fine-tuned iteratively based on community feedback and error analysis.
Example
The Hugging Face Hub allows users to report issues with specific models, fostering a continuous improvement cycle.
7. Transparency and explainability
Root cause
Users often cannot discern whether a model’s output is accurate or fabricated.
Solution
- Confidence scoring: Open source LLMs are designed to provide confidence scores or highlight uncertainties in their responses.
- Explainable AI (XAI): Techniques like attention visualisation and token attribution are used to explain why a model generated a specific output.
- Community education: Open source communities emphasise user education, teaching users how to critically evaluate model outputs.
Example
Tools like Captum (by PyTorch) are used in open source projects to enhance model interpretability.
8. Open benchmarking and evaluation
Root cause
The lack of standardised metrics for hallucination makes it difficult to measure and compare performance.
Solution
- Benchmark datasets: Open source initiatives create benchmark datasets specifically designed to test factual accuracy and hallucination rates.
- Evaluation metrics: Metrics like F1-score, BLEU, and newer hallucination-specific metrics are used to quantify and compare model performance.
- Leaderboards: Open source platforms host public leaderboards to encourage competition and innovation in reducing hallucination.
Example
The HELM (Holistic Evaluation of Language Models) benchmark evaluates LLMs on hallucination and other dimensions.
Privacy concerns in open source LLMs
Privacy concerns arise from the way LLMs are trained, deployed, and used. Open source LLMs, in particular, can exacerbate these issues due to their accessibility and lack of centralised control.
Data leakage and memorisation
- Training data exposure: LLMs are trained on vast datasets, which may include sensitive or private information (e.g., personal emails, medical records, or proprietary data). Open source models make it easier for malicious actors to probe the model and extract memorised data.
- Memorisation risks: LLMs can inadvertently memorise and reproduce sensitive information from their training data, leading to privacy violations.
Lack of data anonymisation
Open source LLMs may not always ensure proper anonymisation of training data, increasing the risk of exposing personally identifiable information (PII).
User data exploitation
When open source LLMs are deployed in applications, they may collect and process user data without adequate safeguards, leading to potential misuse or unauthorised access.
Difficulty in compliance
Open source models may not inherently comply with privacy regulations like GDPR or CCPA, making it challenging for organisations to use them responsibly.
Ethical concerns in open source LLMs
Ethical concerns stem from the potential misuse of open source LLMs, their biases, and the lack of accountability in their development and deployment.
Misuse and malicious applications
- Accessibility to bad actors: Open source LLMs can be easily accessed and modified by malicious actors to create harmful content, such as deepfakes, phishing emails, or disinformation campaigns.
- Lack of safeguards: Unlike proprietary models, open source LLMs may lack built-in safeguards to prevent misuse, such as content filters or usage restrictions.
Bias and fairness
- Bias in training data: Open source LLMs often inherit biases present in their training data, which can lead to discriminatory or unfair outcomes.
- Amplification of harmful stereotypes: Without proper oversight, these models can perpetuate and amplify harmful stereotypes, particularly against marginalised groups.
Lack of accountability
- Decentralised development: Open source projects often involve contributions from many developers, making it difficult to assign accountability for ethical lapses or harmful outcomes.
- Insufficient oversight: Open source LLMs may not undergo the same rigorous ethical review processes as proprietary models, increasing the risk of unintended consequences.
Environmental impact
The computational resources required to train and run LLMs can have a significant environmental footprint. Open source models, if widely adopted without optimisation, could exacerbate this issue.
Intellectual property and attribution
Open source LLMs may inadvertently use copyrighted or proprietary data without proper attribution or licensing, raising ethical and legal concerns.
Future trends
The landscape of artificial intelligence (AI) is rapidly evolving, and open source LLMs are at the forefront of this transformation. As these models become more accessible and sophisticated, several trends are emerging that will shape how individuals, organisations, and societies interact with AI technology.
- Open source LLMs will lower the barriers to entry for AI development. Individuals and organisations without extensive resources will be able to access and utilise advanced language models, fostering innovation across various sectors.
- Users will fine-tune open source LLMs to specialise in specific domains such as healthcare, finance, law, or education. This will lead to models that understand industry-specific terminology and nuances, providing more accurate and relevant outputs.
- With open source LLMs, organisations will deploy models on their own infrastructure. This will mitigate concerns about data privacy, as sensitive information will not need to be sent to external servers for processing.
- Advances in model optimisation are enabling LLMs to run on edge devices like smartphones, tablets, and IoT devices. This will facilitate offline operation and reduce latency, which is crucial for real-time applications.
- Open source LLMs are being trained to support a wider array of languages, including those with limited digital presence. This will promote inclusivity and allow speakers of diverse languages to benefit from AI advancements.
- Open source LLMs can be seamlessly integrated with other open source software, such as content management systems, data analytics platforms, and software development tools, creating robust and flexible solutions.
- Open source projects encourage transparency in model development, allowing for community scrutiny. This will help identify and mitigate biases and unethical use cases early in the development process.
- Open source LLMs will serve as valuable educational tools for students, educators, and researchers. They provide hands-on experience with cutting-edge AI technologies without the need for expensive resources.
- Organisations will reduce costs associated with proprietary software by adopting open source LLMs, reallocating resources to other strategic initiatives.
- Open source LLMs can be developed into modular components that developers can mix and match, enabling the creation of customised AI services tailored to specific needs.
- Open source initiatives often prioritise making models more interpretable. This will help users understand how decisions are made, which is crucial for applications in sensitive areas like healthcare or finance.
- Future open source LLMs may combine text, image, audio, and video processing, leading to more comprehensive AI solutions.
- Open source LLMs can be adapted to run in environments with limited resources. This will provide AI capabilities to educational institutions, NGOs, and small businesses in developing regions.
The future of open source LLMs is vibrant and holds the promise of making AI more accessible, ethical, and versatile. As these trends continue to develop, we can expect a landscape where AI technology is not only advanced but also aligned with the needs and values of a diverse global community.
