technology

Training a Language Model for Text Comparison

Text comparison represents a unique and challenging use case for language models. Unlike tasks such as question answering, searching for information, or generating content, text comparison focuses on analyzing and identifying subtle differences and patterns between two or more pieces of text. This process is geared towards detecting how one text deviates from another, whether in structure, tone, or meaning.

The model’s focus is not on answering questions but rather on recognizing patterns of deviation—an area that traditional models often overlook. These deviations can reveal meaningful insights and are particularly useful in contexts where precision and detail matter. For instance, a text comparison model can identify subtle linguistic shifts, rephrased sections, or even structural differences between similar documents.

This use case stands apart from typical applications like chat, search, and writing assistance. While those tasks focus on interaction, retrieval, or generation, text comparison prioritizes subtle analysis. Detecting nuances often requires a tailored approach, one that emphasizes detail over generalized functionality.

The training process involves equipping the model to capture and interpret these patterns effectively. This requires specialized datasets where textual pairs highlight similarities and differences. Examples might include rephrased paragraphs, altered clauses in contracts, or variations in translated content. Training the model to identify these deviations ensures it is uniquely suited for tasks like plagiarism detection, legal document review, or content consistency verification.

Applications for this type of specialized model are vast. In academia, it can help detect cases of paraphrased plagiarism. In the legal field, it ensures that slight shifts in agreement wording don’t go unnoticed. For content creators working across languages or platforms, the model can maintain consistency with the original material while catching deviations in tone or meaning.

By training a language model specifically for text comparison, we can address challenges that generalized systems struggle to handle. This tailored approach ensures accuracy, reliability, and meaningful insight for industries and tasks that rely on precision. The development of such focused use cases underscores the potential for innovation in language modeling and opens up exciting opportunities for problem-solving in critical domains.

Overcoming the Pitfalls of Generic Domain Models

Domain models are essential tools for structuring complex systems, ensuring they align with the needs of their domain and are usable for developers, users, and stakeholders. However, a frequent problem arises when these models become overly generic, favoring abstract, universal representations over specific, meaningful ones. While high abstraction might seem appealing for flexibility, it can introduce significant challenges in usability, implementation, and maintenance.

When the abstraction level is too high, everything starts to blur into vague concepts like “data” or “thing.” These overly generic representations fail to capture the unique aspects of the domain and offer little guidance for users or developers. The model becomes hard to learn, difficult to interpret, and impossible to understand without extensive documentation. The lack of clarity means that users cannot rely on the model to glean its intended use or functionality—they have to refer to external instructions instead. It sacrifices usability for flexibility in a way that ultimately benefits neither.

In implementation, generic domain models also create complexity. Developers struggle to map abstract concepts to concrete systems, which can lead to errors. Without clear distinctions and definitions, data often gets mixed up or incorrectly applied, increasing the risk of bugs and reducing the system’s reliability. These models also tend to be incompatible across implementations, as different teams interpret them in varying ways, resulting in inconsistencies in how they’re applied.

Striking the right balance between generalization and specificity is critical. If the abstraction is too high, the model becomes generic and error-prone. If it’s too low, it becomes overly tailored to a single use case, losing the flexibility necessary for broader applications. The goal is to find an abstraction level that captures the essence of the domain, while remaining intuitive and adaptable.

To find this optimal balance, clarity should always be prioritized. It’s important to identify the central concepts and relationships unique to the domain and avoid reducing them to overly vague terms. Collaboration with domain experts, users, and developers is also key. These stakeholders can help validate the model’s design, ensuring it meets the needs of its intended audience and aligns with real-world use cases.

Refining the model should be an iterative process. Feedback from users and testing in practical scenarios can reveal whether the abstraction level provides sufficient guidance while maintaining flexibility. Real-world validation ensures that the model works in practice, not just in theory.

Overgeneralized domain models undermine their own purpose. They create unnecessary complexity, increase the risk of errors, and fail to guide developers and users. A well-crafted domain model finds the middle ground—specific enough to provide meaningful structure, yet flexible enough to grow alongside evolving requirements. Thoughtful design and collaboration are the keys to making domain models both effective and intuitive, ensuring they act as strong foundations for long-lasting systems.

Exploring Model Context Protocol (MCP) and kontekst.cloud

Model Context Protocol (MCP) is an open protocol designed to standardize how applications connect with language models (LMs). Think of MCP as being similar to a USB-C port, not for hardware, but for AI-driven systems. It provides a structured way for applications to interact efficiently with data sources, workflows, and tools. The three main features of MCP are resources, prompts, and tools. Resources consist of context and data that the user or model can utilize. Prompts are templated messages and workflows that guide interactions. Tools are functions that a language model can execute to complete specific tasks. This standardized approach makes MCP useful for integrating applications in a clear and repeatable way.

The concepts in MCP have noticeable similarities with kontekst.cloud, a platform that organizes systems around the central concept of “context.” Most features in MCP align directly to kontekst.cloud’s terms. Resources in MCP correspond to content in kontekst.cloud. Tools translate to actions, and prompts could align with agents or actions. However, prompts are tricky to define in kontekst.cloud since they are used differently. One suggestion is to treat them purely as templated messages and separate workflows as their own distinct concept. Unlike MCP, kontekst.cloud introduces threads that capture logs and process information, extending beyond the limited technical logging seen in MCP. This ability to store execution histories helps define workflows and track processes in greater detail.

Some challenges exist with terms like “resources” and “data,” as they are too broad and often end up encompassing everything. Kontekst.cloud has made efforts to be more precise by splitting features into content, process data, and actions. The platform uses an endpoint called /data to store all information related to features, but alternatively, /resources could be used. However, the generic nature of these terms still poses some risk of overlap between concepts. Despite this, the flexibility built into kontekst.cloud allows substantial customization, which makes implementing MCP on the platform relatively straightforward.

Kontekst.cloud’s design also enables support for alternative protocols like SOLID or other semantic web technologies. By adding a compatible layer, the platform can easily integrate standards like MCP while retaining the ability to work with other options. This adaptability positions kontekst.cloud as a versatile tool for building interoperable systems. Whether working with structured standards like MCP or experimenting with decentralized architectures supported by protocols like SOLID, kontekst.cloud provides the foundation for highly flexible implementations.

An important distinction between MCP and kontekst.cloud lies in the concept of context itself. In kontekst.cloud, context operates as the central organizing principle and can be seen as the “server” that ties together content, actions, workflows, and threads. MCP lacks this central concept and instead ties resources and tools to individual servers. To bridge this gap, kontekst.cloud could represent each context as its own independent server, assigning a root URL to each. This modular approach enhances scalability and allows workflows to be tied directly to user-specific or application-specific contexts, creating a more personalized experience.

Although MCP excels as a standardized integration protocol, kontekst.cloud takes these concepts further by emphasizing context as the foundation for organizing data and processes. This focus enables richer workflows and simplifies the design of reusable systems. With its ability to support MCP and other protocols, kontekst.cloud isn’t limited by any single system but instead embraces interoperability as a core strength. By combining the standardization provided by MCP with the context-driven modularity of kontekst.cloud, developers can build more scalable and flexible applications tailored to diverse needs.

Stories – A Way to Transfer Knowledge

Since the dawn of civilization, storytelling has been our primary way of sharing and preserving knowledge. From oral traditions filled with myths and legends to written texts, films, and interactive media, stories have shaped how we understand the world.

But why are stories so effective? Because they create experiences. Instead of just presenting isolated facts, they embed knowledge in a context, making it easier to understand, remember, and apply. This principle isn’t just useful for humans—it can also transform how we train language models.

How Stories Shape Learning

Stories are more than just entertainment. They act as cognitive frameworks, helping us connect new information to what we already know. Think about how we learn history—not through a list of dates and events, but through narratives about the people who lived them. The same applies to scientific discoveries, moral lessons, and even problem-solving strategies.

By structuring knowledge within a story, we make it relevant and engaging. A well-crafted narrative provides context, emotion, and meaning, making learning a natural and immersive experience.

Using Stories to Train Language Models

The way we train language models today often relies on vast amounts of structured and unstructured data. But what if we approached this process more like teaching a human?

Instead of feeding language models disconnected data points, we can frame information within meaningful stories. This method allows the model to understand not just words and syntax but also the deeper relationships between concepts. Context-rich learning could lead to more intuitive and adaptable language models, capable of reasoning and responding in more human-like ways.

A Future Built on Narrative Learning

Imagine a world where language models learn through carefully curated stories—absorbing knowledge in the same way we do. This could revolutionize fields like education, research, and communication.

By embracing storytelling as a core method for training, we’re not just improving language models. We’re reinforcing the fundamental truth that knowledge, when placed in the right context, becomes something more than just data—it becomes wisdom.

How to Catch Hidden Assumption Errors in Your Code—And Can a Language Model Help?

Every developer has encountered a bug that “shouldn’t have happened.” Often, these bugs stem from hidden assumptions in the code.

Take the example of a system handling substitute employees. It assumes that every substitute is assigned to replace someone. But in reality, substitutes may exist because no one currently holds the position. This faulty assumption leads to a null pointer exception, and a database constraint failure makes things worse.

These issues could have been caught earlier. But how? Could a language model (LM) help uncover such flawed assumptions before they break production?

Understanding Assumption-Based Bugs

An assumption-based bug happens when code is built on an unchecked belief about how the system works.

In the substitute employee example:

The system assumes every substitute has a direct assignment.
In some cases, no one holds the position, making the assumption false.
This leads to a null pointer exception and a database constraint failure.

Such bugs are common because assumptions often go unchallenged during development.

Can a Language Model Help Detect These Issues?

LMs could assist by:

Extracting assumptions from code and documentation.
Identifying weak spots, comparing them to past failures.
Suggesting fixes, pointing out missing cases or alternative logic.

While today’s LMs aren’t perfect at reasoning, they can help detect patterns and highlight potential problem areas.

Practical Ways to Reduce Assumption-Based Bugs

Even without an LM, there are ways to catch these issues early:

Document Assumptions – Clearly state system assumptions and challenge them.
Use Static Analysis Tools – Linters and type checkers can catch logic inconsistencies.
Implement Defensive Programming – Always check for null values and validate inputs.
Explore AI-Assisted Code Review – Emerging tools can help flag logical inconsistencies.

Conclusion

Many software bugs come from flawed assumptions rather than syntax errors. While LMs may assist in uncovering them, developers can take proactive steps today: document assumptions, use static analysis tools, and test for edge cases.

Design Alternatives for Using Path, HTTP Methods, and Actions

When designing an API, choosing how to structure endpoints and model the interaction between client and server is a critical design decision. The three alternatives outlined – data-driven, object-oriented, and action/process-driven – represent different approaches with distinct strengths and weaknesses. The choice of approach should be based on both technical and business needs, as well as user expectations and workflows.

1. Data-Driven Approach

Description

This approach focuses on data as the primary entity in the API. Clients perceive the API as a system for storing and retrieving data, without directly interacting with actions or processes. Business logic and processing happen invisibly on the backend, and clients only see the results through the data produced.

Characteristics

Clear separation between data and processes.
Clients interact only with resources (e.g., submissions) and their lifecycle.
Process statuses are represented as fields in the data.
Resembles a CRUD (Create, Read, Update, Delete) approach.

Advantages

Simple for clients – they only retrieve and store data without needing to understand domain logic.
Fewer endpoints with a consistent URL structure.
Well-aligned with REST principles.

Disadvantages

Business logic can be difficult for clients to understand and discover.
Risk of logic being spread across clients if the API does not provide enough guidance.
Less suitable for complex processes involving multiple steps or data types.

Example

GET /data/submission
GET /data/submission/1199930
POST /data/submission
PATCH /data/submission/1199930

When is this approach suitable?

For simple systems where processes are not highly complex.
When clients primarily work directly with data (e.g., case handlers).
When minimal coupling between clients and domain-specific business logic is desired.

2. Object-Oriented Approach

Description

In this approach, each resource is treated as an object that has both data and associated operations (methods). Clients can not only retrieve and update data but also trigger specific actions on each resource. This makes business logic more explicit in the API.

Characteristics

Each resource has its own set of operations/actions.
Clients must understand domain-specific concepts and processes.
The approach resembles object-oriented systems, where objects have methods.

Advantages

Clearer process support – clients receive explicit signals about available actions.
Easier for clients to navigate business logic.
Well-suited for resources with many specific actions governed by business rules.

Disadvantages

Can lead to an explosion of endpoints when multiple resources have multiple actions.
Maintaining a consistent structure across various object types can be challenging.
Can become cumbersome if many actions are not resource-specific but apply across multiple resources.

Example

POST /data/submission/search
POST /data/submission/submit
POST /data/submission/1199930/selectPractice
POST /data/submission/1199930/cancel

When is this approach suitable?

When resources have specific, business-related operations.
When it is important for clients to understand the processes around resources.
When the API is part of a larger domain application with domain-oriented users.

3. Action/Process-Driven Approach

Description

This approach explicitly separates actions from data. Clients retrieve and manage data in one way, while business processes and operations are modeled as separate process resources or services. This allows actions to involve multiple data types simultaneously and handle more complex workflows.

Characteristics

Clear distinction between data and processes.
Processes have dedicated endpoints that handle multiple resources and complex logic.
Suitable for larger, cross-cutting processes.
Often inspired by Command-Query Responsibility Segregation (CQRS).

Advantages

High flexibility in modeling business logic.
Easier to version or modify process logic without changing data models.
Well-suited for systems with complex, multi-step workflows.

Disadvantages

Can create uncertainty about which data the processes operate on.
Requires more documentation and client adaptation.
May result in an artificial separation of data access and process handling, even when logically connected.

Example

POST /process/submitReimbursementClaim
POST /process/updateReimbursementClaim
POST /search

When is this approach suitable?

When processes involve multiple different data types.
When processes have high complexity and multiple steps.
When processes should function as “black box” operations with clear input and output.
When supporting both manual and automated workflows via the same interface.

Summary Evaluation

Approach	Client Simplicity	Flexibility	Process Support	Suitable for Complex Domains
Data-Driven	✅ Very simple	❌ Limited	❌ Weak	❌ Not well-suited
Object-Oriented	⚠️ Moderate	⚠️ Moderate	✅ Good	⚠️ Partially suitable
Action/Process-Driven	⚠️ Requires learning	✅ High	✅ Very good	✅ Highly suitable

Recommendation

Choosing an approach should be based on:

The complexity of the domain.
How self-sufficient clients need to be.
How clearly processes need to be defined for clients.
Whether the API is primarily a CRUD interface or a process-driven system.

In many cases, a hybrid model may be the best solution, where basic data is managed using a data-driven approach, while more complex workflows are exposed via process-driven endpoints. This provides both simple data handling and flexible process support.

Knowledge-Augmented Model Training (KAMT)

Knowledge-Augmented Model Training (KAMT) is a structured approach to transforming a Foundation Language Model (FLM) into a Specialized Language Model (SLM) by incorporating domain-specific knowledge. This process leverages Knowledge Packs (KPs)—curated datasets containing expert-level information—to enhance the model’s proficiency in targeted areas.

By systematically integrating structured knowledge, KAMT ensures that AI models maintain their foundational language capabilities while gaining deep expertise in specific fields. This makes it a powerful strategy for organizations looking to build high-performance AI systems without the need to train models entirely from scratch.

Key Components of KAMT

1. Foundation Language Model (FLM)

At the core of KAMT is the FLM, a pre-trained general-purpose language model with broad linguistic knowledge. This model serves as the starting point and provides strong baseline capabilities in natural language understanding and generation. However, its general nature means it lacks deep expertise in specialized areas.

2. Knowledge Packs (KPs)

Knowledge Packs (KPs) act as modular data units containing structured domain-specific information. These are designed to systematically enhance the FLM’s knowledge in a particular field. A KP may include:

Industry-Specific Literature – Research papers, textbooks, whitepapers
Technical Documentation – Manuals, software documentation, engineering specifications
Expert-Curated Datasets – Annotated corpora, structured knowledge bases
Real-World Data – Case studies, financial reports, patient records (where applicable)
Interactive Feedback – Human-in-the-loop refinements and reinforcement learning

3. Specialization Training Process

KAMT involves a structured fine-tuning process that adapts the FLM using the KPs. The key steps include:

Supervised Fine-Tuning – The model is exposed to high-quality labeled data to refine its accuracy in a given domain.
Reinforcement Learning with Human Feedback (RLHF) – Expert reviewers evaluate and adjust the model’s outputs to improve reliability.
Knowledge Injection Techniques – The model learns to integrate structured knowledge without erasing its foundational understanding.
Task-Specific Optimization – The SLM is fine-tuned for specialized applications such as legal analysis, medical diagnosis, or scientific research.

4. Specialized Language Model (SLM)

The result of KAMT is a Specialized Language Model (SLM)—a version of the FLM that is finely tuned for a specific domain. The SLM offers:
✅ Enhanced Accuracy – Greater precision in handling complex domain-specific queries.
✅ Deep Context Understanding – Improved comprehension of industry terminology and specialized concepts.
✅ Task-Specific Adaptability – Optimized for use cases such as research assistance, legal document processing, medical diagnosis, or financial modeling.
✅ Scalability and Continuous Learning – Additional KPs can be integrated over time, keeping the model up to date with new knowledge.

Why Use KAMT?

KAMT provides a scalable, cost-effective, and modular approach to AI specialization. Instead of building models from scratch, organizations can leverage pre-trained FLMs and enhance them with domain knowledge, resulting in a faster, more efficient, and adaptable AI solution.

Use Cases

Healthcare & Medicine – Specialized AI for medical diagnostics, patient data analysis, and research.
Law & Compliance – AI systems that understand legal language, contracts, and regulatory requirements.
Finance & Trading – AI-driven market analysis, risk assessment, and fraud detection.
Engineering & Technology – Enhanced AI assistants for software development, manufacturing, and automation.
Education & Research – Custom AI tutors and academic research assistants.

Conclusion

Knowledge-Augmented Model Training (KAMT) is a powerful paradigm for AI specialization, bridging the gap between general-purpose language models and expert-level AI systems. By leveraging KPs and targeted training processes, organizations can rapidly develop domain-specific AI models that offer superior accuracy, contextual understanding, and adaptability in real-world applications.

European Union Launches OpenEU-LM: The First Truly Open and Efficient Language Model Matching the Best in AI

Here’s a vision of a press release for the announcement of OpenEU-LM:

FOR IMMEDIATE RELEASE

European Union Launches OpenEU-LM: The First Truly Open and Efficient Language Model Matching the Best in AI

Brussels, [Date] – The European Union today announces the first release of OpenEU-LM, a groundbreaking large language model (LLM) that rivals industry leaders such as GPT-4, Gemini, and DeepSeek while setting new standards in openness, adaptability, and efficiency.

Developed as part of the EU’s commitment to technological sovereignty and transparency, OpenEU-LM is the first fully open-source language model where the entire development process—including tools, code, and training data—is publicly available. Anyone can not only access the model but also reproduce its training from scratch, ensuring maximum transparency and fostering innovation across Europe and beyond.

Key Advantages of OpenEU-LM:

Truly Open Source: Unlike proprietary models, OpenEU-LM allows researchers, businesses, and developers full access to its architecture, datasets, and training methodologies.
Domain-Specific Adaptability: The model can be customized for specialized domains—such as healthcare, law, and finance—without requiring a full retraining process.
Unprecedented Efficiency: OpenEU-LM’s training process demands just 1/1000th of the hardware and energy consumption compared to other state-of-the-art LLMs.
Minimal Compute Requirements: Once deployed, OpenEU-LM can run on 1/10,000th of the hardware resources typically needed for similar AI models, making it an ideal choice for edge computing and energy-efficient applications.
Enterprise Cloud Service: To support businesses and public institutions, OpenEU-LM will also be offered as a secure, high-performance cloud service across the EU.

A Milestone for AI in Europe

OpenEU-LM represents the EU’s commitment to ethical, sustainable, and inclusive AI development. By eliminating reliance on closed-source, resource-intensive AI models, OpenEU-LM empowers governments, startups, and enterprises with a transparent and customizable alternative that aligns with Europe’s digital strategy.

“OpenEU-LM is more than just a language model—it is a declaration of technological independence and innovation,” said [EU Official]. “With this initiative, we are ensuring that AI in Europe is open, accessible, and built to serve the public good.”

Availability and Next Steps

The first release of OpenEU-LM is available today at [website/repository link], where developers, researchers, and enterprises can access, test, and contribute to its continuous improvement. Enterprise cloud solutions will be launched in Q3 2025.

For more information, visit [official EU AI page] or contact [press contact details].