Research desk
An annotated reading list. Every entry is credited to its original authors, with a note on what it changes once the idea meets a real system.
3 entries
Demonstrates that a sufficiently large language model can perform a new task from a handful of examples in the prompt, with no gradient updates. The paper established in-context learning as the default interface to a language model.
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.
The architectures and pre-training results everything else stands on.
3 entries
Grounding a model in documents you actually own.
4 entries
Getting a model to show its work, and checking the work.
5 entries
Models that call tools, hold state, and finish multi-step jobs.
6 entries
Changing what a model does without retraining all of it.
3 entries
Steering outputs toward what a person would accept.
3 entries
How you know it works before a customer finds out it does not.
5 entries