Improved training data quality enhances closed LLM responses
New research demonstrates that high-quality training data is critical for improving the question-answering capabilities of closed language models, surpassing the impact of architectural changes.
What happened?
Researchers investigated the possibility of integrating documents directly into the weights of a 4-bit Gemma-4-e4b model using LoRA. The objective is for the system to answer questions about a corpus without external retrieval or context windows (closed-book QA). The study comprised approximately 100 training runs, ranging from individual documents to a corpus of 99 documents.
Key facts
| LLM-modell | Gemma-4-e4b (4-bit) |
|---|---|
| Antal träningskörningar | Cirka 100 |
| Korpustorlek | 1 till 99 dokument |
| Noggrannhetsökning efter kurering (15 dokument) | 57,7% till 85,7% |
”once adapter capacity is adequate, training-data quality is the dominant lever on closed-book accuracy, outweighing LoRA rank, learning rate, and two alternative architectures combined”
”A single curation pass (shortening gold answers to canonical 1-6 word spans and dropping trivia) moved closed-book accuracy from 57.7% to 85.7% on a 15-document corpus, a larger jump than any architectural change.”
Why it matters
The results indicate that data quality is the most important factor for accuracy in closed-book QA, provided the adapter has sufficient capacity. This outweighs the impact of LoRA rank, learning rate, and alternative architectures. Capacity acts as a hard limit; below a certain level, no data intervention is effective.
Who is affected?
The research primarily affects developers and researchers in AI and machine learning working with language models. The findings are relevant for those seeking to build more efficient and compact LLMs for tasks requiring internalised knowledge.
What else you should know
Simple curation, where gold-standard answers were shortened to 1-6 words and trivial information was removed, increased accuracy from 57.7% to 85.7% for a 15-document corpus. This represents a more significant improvement than any architectural modification.
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