General-purpose models like GPT-4o handle news summaries well, but on a legal contract they miss key clauses—force majeure or notice period. Our practical experience shows that domain adaptation dramatically changes quality. Fine-tuning on domain-specific data teaches the model to highlight what matters in your context, be it medical records, court rulings, or technical documentation. Our engineers adapt architectures to task specifics: style, terminology, and structure. The result is reliable summarization. Inference cost savings compared to commercial LLMs can reach 10x; for example, fine-tuning ruT5 on 3000 pairs costs about $500 and reduces monthly inference costs by 70%. Contact us for a free project assessment.
What Problems Does Fine-tuning Solve?
Style Mismatch. Generative models tend toward conversational phrasing. Business reports need formal language, news requires brevity, and medical texts demand precise terminology. Fine-tuning adapts the style to the domain, leveraging transfer learning.
Factual Hallucinations. Models may insert non-existent figures or names, critical in legal and financial contexts. Domain-specific fine-tuning reduces hallucination rates by 30–50%. Research indicates that adapted models produce 20% fewer fabricated details.
Long Context. Documents like court rulings exceed 8K tokens. T5 and BART with 1024-token windows require chunking—we use a sliding window strategy with overlap to preserve context.
How We Adapted the Model for Legal Documents
A client—a legal department at a large company—needed to summarize supply contracts. Standard models (GPT-4o) produced paraphrases rather than extracts, missing dates, amounts, and termination conditions.
Our approach:
- Collected 2000 contract-summary pairs from a 5-year archive.
- Used ruT5-base with LoRA (rank 16, target modules q_proj, v_proj).
- Trained on one A100 80GB, 3 epochs, batch size 8, gradient accumulation 2. Loss converged in 2 hours.
- After fine-tuning, ROUGE-1 increased from 0.32 to 0.48, BERTScore from 0.78 to 0.89.
- Human evaluation: accuracy 4.2/5, completeness 4.5/5.
Result: The model extracts all essential conditions with hallucinations below 2%. This demonstrates that fine-tuning T5 for domain-specific summarization yields ROUGE scores 1.8x higher than the base model.
Architecture Comparison for Summarization
| Model | Language | Context Length | Quality After Fine-tuning | Inference Speed |
|---|---|---|---|---|
| T5-base | EN/RU | 512-1024 | Good (especially with prefix tuning) | Medium |
| BART-base | EN | 1024 | Excellent (designed for generative tasks) | Medium |
| mT5-base | Multi | 512-1024 | Satisfactory (requires more data) | Low |
| LLaMA-3 8B | EN | 8192 | High (but expensive) | Slow |
| Metric | Before Fine-tuning | After Fine-tuning | Gain |
|---|---|---|---|
| ROUGE-1 | 0.32 | 0.48 | +0.16 |
| ROUGE-2 | 0.18 | 0.32 | +0.14 |
| ROUGE-L | 0.28 | 0.43 | +0.15 |
| BERTScore | 0.78 | 0.89 | +0.11 |
For Russian domains, we recommend ruT5 or ruBART—they are compact and achieve strong performance with 2000+ examples. After fine-tuning, these models retain document structure 1.5x better than multilingual counterparts. Choosing the right architecture yields significant cost savings.
Why Fine-tune on Domain-specific Data?
Training on a general corpus like Common Crawl does not capture your business specifics. Terms, document structure, and field importance require adjustment. Fine-tuning for domain adaptation teaches the model that "material terms" in a contract differ from "weather" in news. In practice, an adapted model achieves ROUGE-L 15–20 points higher than the base model.
Data Requirements for Quality Fine-tuning
Our experience shows that 1000–5000 pairs are enough to gain 15–20 ROUGE points. With less data, we use augmentation: back-translation, synonym replacement, and recursive summarization via GPT-4o. Synthetic data cannot replace real data—always mix them in a 1:3 ratio. Evaluation uses BERTScore alongside ROUGE. Domain adaptation NLP is key for transferring knowledge to new domains.
Process
- Domain and requirements analysis—collect a representative sample, define style and summary length.
- Data preparation—cleaning, labeling (if pairs missing), chunking for long documents using concatenation with a separator and sliding window.
- Architecture selection and fine-tuning—experiment with T5, BART, sometimes mT5. Tune hyperparameters: learning rate (1e-4…5e-5), batch size, epochs. Use EarlyStopping from Hugging Face.
- Evaluation—ROUGE + BERTScore + human evaluation. If quality is below threshold, revisit data or architecture.
- Deployment—serve via Triton Inference Server or FastAPI with p99 latency optimized to 500ms. Use INT8 quantization for GPU acceleration.
What’s Included
- Analysis of your data and domain with architecture selection.
- Preparation and augmentation of the training set.
- Fine-tuning with hyperparameter optimization.
- Evaluation via ROUGE, BERTScore, and human testing.
- Model deployment (Triton, FastAPI) with documentation.
- Training your team to work with the model.
- Stability guarantee—additional epochs if needed.
- Deliverables include model weights, inference scripts, API documentation, and a support period of 3 months.
Common Fine-tuning Mistakes
- Overfitting with small data. Use dropout=0.1, early stopping, LoRA/PEFT.
- Ignoring preprocessing. Text must be cleaned of broken characters, stop words (not for ROUGE), and normalized.
- Incorrect chunking. Documents longer than 1024 tokens should be split with 128-token overlap.
- Evaluation by ROUGE only. ROUGE does not capture semantic similarity—always add BERTScore.
Contact us for a consultation: we will help assess your project, select architecture, and estimate the budget. We bring 7+ years of experience and 30+ NLP and summarization projects. Request a preliminary analysis—we will demonstrate the quality improvement fine-tuning can achieve on your data.