Background to the issue
Rankify's modular design and rigorous evaluation process dramatically improves the "illusion" problem that retrieval-enhanced generation (RAG) systems often face when the generated content does not match the retrieved document.
Implementation steps
- Data preparation::
- Select domain-adapted datasets:
Dataset("nq-dev").download() - Document preprocessing ensures uniform formatting
- Select domain-adapted datasets:
- skill set::
- Semantic search using Contriever (avoiding keyword limitations)
- Contextual reordering using RankGPT (considering inter-document associations)
- Configure the LLaMA-3 generator:
Generator("meta-llama/Llama-3.1-8B")
- Evaluation Optimization::
- adoption
metrics.calculate_generation_metrics()Calculating EM scores - pass (a bill or inspection etc)
n_docsNumber of reference documents for parameter tuning (5-10 recommended)
- adoption
best practice
Empirical tests show that the three-phase scheme combining ColBERT search + MonoT5 reordering + GPT-4 generation can achieve an accuracy of 78.31 TP3T on the HotPotQA dataset, which is 221 TP3T higher than the baseline.
This answer comes from the articleRankify: a Python toolkit supporting information retrieval and reorderingThe
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