Catalog
concept#AI#Machine Learning#Data

Language Model (LM)

A model that learns probabilities of word sequences to generate, complete, or classify text. Language models underpin modern NLP applications and vary widely in architecture, training data, and controllability.

A language model (LM) is a statistical or neural system that learns probabilities over word sequences to generate, complete, or classify text.
Established
High

Classification

  • High
  • Technical
  • Technical
  • Intermediate

Technical context

API gateways and authentication servicesData lakes and feature storesObservability and monitoring stacks

Principles & goals

Data quality first: training data determine behavior and biases.Transparency and traceability: outputs should be explainable and auditable.Safety-oriented design: integrate safety and moderation rules early.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Privacy risks when using personal data in training.
  • Abuse via generation of misleading or harmful content.
  • Technical dependency on proprietary providers and models.
  • Test with realistic prompts and adversarial examples.
  • Document datasets and analyze provenance and bias.
  • Roll out gradually with observability and human review.

I/O & resources

  • Training corpus (text data)
  • Compute resources (GPU/TPU)
  • Evaluation and test datasets
  • Generated or classified text
  • Confidence and quality metrics
  • Logs and auditing information

Description

A language model (LM) is a statistical or neural system that learns probabilities over word sequences to generate, complete, or classify text. It underpins text generation, translation, question answering, and conversational agents. Models differ by architecture, training data, capacity, and controllability.

✔

  • Automation of language tasks and efficiency gains.
  • Scalable generation and extraction of textual information.
  • Versatility: a model can be adapted for many NLP use cases.

✖

  • Hallucinations and inaccurate factual output are possible.
  • High resource requirements for training and large models.
  • Dependence on training data and associated biases.

  • Perplexity

    Measure of a model's uncertainty in probability predictions.

  • BLEU / ROUGE

    N-gram based metrics to assess generation quality against references.

  • Latency and throughput

    Operational metrics for response time and processed requests per second.

Customer support assistant

A company uses a fine-tuned LM to automatically answer frequent inquiries with escalation path to humans.

Automated content generation

Marketing teams generate drafts and variants of product texts that are later editorially reviewed.

Research assistance

Analysts use LMs to extract and condense relevant information from large document sets.

1

Define goals and success criteria; create data inventory.

2

Model selection, prototyping and evaluation with representative data.

3

Production deployment, monitoring, feedback loop and governance setup.

⚠️ Technical debt & bottlenecks

  • Outdated training datasets without versioning and tests.
  • Monolithic integration instead of modular inference pipelines.
  • No automated tests for safety and bias checks.
Data collection and annotationCompute capacity for training and inferenceModel monitoring and quality evaluation
  • Use for legal advice without human review.
  • Automatically publishing generated content without fact-checking.
  • Training with unauthorized or personal data.
  • Underestimating inference costs at large production volume.
  • Not accounting for model drift and necessary retraining.
  • Lack of governance for handling harmful outputs.
Machine learning and NLP expertisePrivacy and compliance knowledgeDevOps for model deployment and monitoring
Availability and quality of training dataLatency and throughput requirements for real-time servicesSafety and moderation requirements
  • • Privacy and compliance regulations
  • • Budget for infrastructure and licensing costs
  • • Availability of suitable training data