AI0-001 AI Implementation and Operations Practice Question
A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model performs well on test data but produces summaries that include hallucinated legal clauses. Which mitigation strategy is most effective?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement retrieval-augmented generation (RAG) to provide factual context.
Implement retrieval-augmented generation (RAG) to provide factual context. RAG reduces hallucinations by allowing the model to retrieve relevant, factual information from an external knowledge base during generation, grounding its output in verified data. Option A (different tokenizer) does not address the core issue of factual accuracy. Option B (decrease temperature) affects randomness but does not prevent the model from fabricating content. Option D (max token limit) truncates output but does not stop the model from including false information within that limit.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different tokenizer during fine-tuning.
Why it's wrong here
The tokenizer governs text segmentation into tokens; it does not alter the model's learned factual grounding, so hallucinated clauses persist. It is tempting because tokenizer mismatches do cause training artefacts, but retrieval augmentation or grounding constraints address fabrication directly.
- ✗
Decrease the temperature parameter to 0.1 during inference.
Why it's wrong here
Lowering temperature makes token selection more deterministic but does not supply the missing legal grounding, so fabricated clauses remain likely. It is tempting because low temperature reduces random variation, yet hallucination here is a knowledge problem, addressed by retrieval augmentation or constrained decoding against source documents.
- ✓
Implement retrieval-augmented generation (RAG) to provide factual context.
Why this is correct
Retrieval-augmented generation grounds each summary in retrieved source passages, so the model conditions on actual clause text rather than parametric memory. This directly targets the hallucinated clauses arising after fine-tuning, satisfying the requirement to supply factual context at inference time without retraining the model.
- ✗
Set a maximum token limit of 50 for each summary.
Why it's wrong here
A 50-token cap only truncates output length; the model still generates unsupported clauses within those tokens. It is tempting because shorter summaries reduce exposure to invented content, but hallucination stems from ungrounded generation, so retrieval-augmented or citation-constrained decoding is required.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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