Grounding a model in documents you actually own.
Language models attend strongly to the beginning and end of a long context and weakly to the middle. The authors show accuracy degrading in a characteristic U-shape as the position of the relevant passage moves through the input, even in models explicitly built for long contexts.
A collection of retrieval datasets spanning very different domains and task types, used to test whether a retriever that works on one corpus still works on another without retraining. The headline finding is that sparse lexical baselines remain hard to beat out of domain.
Pairs a pretrained generator with a neural retriever over an external corpus, so answers can draw on documents that were never in the training data and can be updated by changing the corpus rather than retraining the model.
Replaces term matching with a dual-encoder that embeds questions and passages into the same vector space. Relevance becomes a nearest-neighbour lookup, which lets retrieval find passages that share no vocabulary with the question.