Deduplication
Calibrated automation plus human review of retained and discarded content.
LLM Training Data Curation Services
Prepare LLM pre-training and fine-tuning corpora through deduplication, quality filtering, benchmark decontamination, privacy handling, provenance review and domain balancing.
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What Is LLM Training Data Curation?
Curation turns a raw corpus into a traceable, measurable training asset. It is distinct from LLM and RLHF annotation, which creates alignment and evaluation signals after corpus preparation.
The LLM Curation Stack
Quality filtering
Calibrated automation plus human review of retained and discarded content.
Boilerplate removal
Calibrated automation plus human review of retained and discarded content.
Benchmark decontamination
Calibrated automation plus human review of retained and discarded content.
PII scrubbing
Calibrated automation plus human review of retained and discarded content.
Safety filtering
Calibrated automation plus human review of retained and discarded content.
Language identification
Calibrated automation plus human review of retained and discarded content.
Synthetic-content assessment
Calibrated automation plus human review of retained and discarded content.
Licence and provenance review
Calibrated automation plus human review of retained and discarded content.
Domain composition analysis
Calibrated automation plus human review of retained and discarded content.
Every Filter Removes Something You Wanted
Over-filtering silently erases specialist, multilingual and symbol-dense material. eQOURSE reviews samples from both retained and discarded sets, then reports retention by domain, source and language.
Benchmark Decontamination
Exact, n-gram and fuzzy matching checks public or private evaluation suites so results measure unseen capability instead of memorisation.
Licence, Provenance and Synthetic-Content Assessment
Source, licence, consent, collection context, transformation lineage and exclusions are documented. Synthetic-text classifiers provide uncertain flags for human review, never an automatic deletion verdict. Licence documentation is not legal advice.
Our LLM Data Curation Process
- Profile the corpus
- Define the training objective
- Design the pipeline and retention floors
- Calibrate retained and discarded samples
- Process at scale
- Review composition and contamination
- Deliver the corpus, manifest, exclusions and re-runnable configuration
What You Get Back
Retention by stage, domain and source; duplication and contamination reports; composition analysis; an exclusion register; provenance and lineage; PII verification samples; and reusable pipeline configuration.
Global Corpora with India-Wide Language Depth
Programmes support 30+ global languages with native reviewers and comprehensive Indian regional-language, code-mixed and romanised coverage.
Security, Formats and Delivery
JSONL, Parquet, WARC-family files, text collections, cloud exports, Hugging Face datasets and custom schemas, processed in your environment or ours under agreed access and retention controls.
Related AI Data Services
Data Cleaning & Validation Dataset QA & Label Audit Text Data Collection AI Model Testing Cleaned dataset samples
Frequently Asked Questions About LLM Data Curation
What is LLM training data curation?
Curation is everything between having a corpus and being able to train on it: deduplication, quality filtering, benchmark decontamination, PII scrubbing, licence and provenance review, and measuring corpus composition.
Why does curation matter more than corpus size?
A well-curated smaller corpus can be more useful than a larger raw one because duplicated and low-quality content wastes compute and teaches unwanted patterns.
What is over-filtering and why is it dangerous?
Over-filtering removes content you needed. It is hard to see because the final corpus only shows what survived, while specialist, multilingual and symbol-dense content may have disappeared.
How do you prevent over-filtering?
We sample and human-review both retained and discarded sets at each calibrated stage, then report retention by domain, source and language so disproportionate removal becomes visible.
What is benchmark decontamination?
It removes evaluation-benchmark content from the training corpus so benchmark results measure unseen capability rather than memorisation.
Is exact matching enough for decontamination?
No. Benchmark items can be reformatted, translated, paraphrased or split. Exact matching should be combined with n-gram, fuzzy and question-only or answer-only checks.
Can you check private benchmarks?
Private benchmarks can be checked under an agreed NDA and handling policy. They should not be retained after the contamination check when that is contractually required.
How do you handle deduplication?
Typical workflows combine exact hashes, MinHash and LSH for near-duplicates, and similarity methods where appropriate. Thresholds must be tuned and reviewed on the actual corpus.
Can you detect AI-generated content in a corpus?
We can flag likely synthetic text using multiple signals and human review, but detection remains uncertain. We report estimates and uncertainty rather than silently deleting content on a classifier verdict.
Do you review licensing and provenance?
We can document source, licence, consent status, collection method, transformation lineage and exclusions so legal counsel has the facts needed for a decision. This is not legal advice.
Do you handle non-English corpora?
Yes, across 30+ global languages with native reviewers and comprehensive Indian regional-language, code-mixed and romanised coverage.
Do you run the processing infrastructure?
Processing can run in your environment or ours. The core value is pipeline design, threshold calibration and human review of what each stage keeps and removes.
Which formats do you support?
Inputs include JSONL, Parquet, WARC-family files, text collections, cloud storage exports and Hugging Face datasets. Outputs can include JSONL, Parquet, sharded datasets and custom schemas with per-document metadata.
What determines curation cost?
Corpus size, filtering stages, domain specificity, languages, benchmark scope, provenance depth, review intensity, processing environment and turnaround all affect cost.
How do we start?
Start with a corpus audit: profiling, duplication rate, contamination check and composition report before any content is filtered.