A new research paper explores methods for large language models (LLMs) to access new information beyond their pre-training data. The study compares representation-based (KV-cache) and parametric (fine-tuning) adaptation techniques across five knowledge-intensive benchmarks. Results indicate that KV-cache based methods, specifically 'Cartridges', offer superior accuracy at various storage budgets, outperforming parametric methods by up to 10 points in an oracle setting and significantly in multi-document retrieval scenarios. However, Cartridges, like full fine-tuning, can lead to catastrophic forgetting on control benchmarks. AI
IMPACT This research provides insights into optimizing LLM knowledge integration, potentially improving efficiency and accuracy for knowledge-intensive tasks.
RANK_REASON Research paper published on arXiv detailing methods for LLM knowledge injection. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- Cartridges
- DagsHub
- Gotit.pub
- Hugging Face
- KV cache
- MLP adapters
- Nathanaël Carraz Rakotonirina
- ScienceCast
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