Retrieval-Augmented Generation for Natural Language Processing: A Survey (2024 · 86 citations)
Large language models store knowledge implicitly in their weights. As a result, they can produce fluent but false answers, they need costly retraining to refresh facts, and they often lack depth in specialised domains. Retrieval-augmented generation (RAG) addresses these issues by consulting an external knowledge…
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding (2026 · 1 citation)
When LLMs answer questions or summarize from a supplied passage, they often fall back on knowledge memorized in pretraining.
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026 · 1 citation)
LLMs can adapt to a user's tone and topical interests, but current personalization methods (prompting, retrieval of user profiles, fine-tuning) treat this ability as a black box.
GlossaGen: Making Academic Translation Smarter with Glossing (2026)
Non-native readers of English scientific papers need help with comprehension, but they also need to learn the field's vocabulary.

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Updated 1 November 2026