Research desk
The papers behind how we build
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.
All entries
2 entries
- 2021ICLR 2022PublishedCurated
LoRA: Low-Rank Adaptation of Large Language Models
Freezes the pretrained weights and injects trainable low-rank matrices into each layer instead. The number of trainable parameters drops by several orders of magnitude, adapters are small enough to swap at runtime, and training cost falls with them.
- 2021NeurIPS 2021PublishedCurated
BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models
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.
Browse by topic
Foundations
The architectures and pre-training results everything else stands on.
3 entries
Retrieval & RAG
Grounding a model in documents you actually own.
5 entries
Reasoning
Getting a model to show its work, and checking the work.
6 entries
Agents & Tool Use
Models that call tools, hold state, and finish multi-step jobs.
8 entries
Adaptation
Changing what a model does without retraining all of it.
4 entries
Alignment
Steering outputs toward what a person would accept.
6 entries
Evaluation
How you know it works before a customer finds out it does not.
11 entries