5 citations · 6 across the 5 of their papers we have counts for
9 papers · 1 filter
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian +6
Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging "generalist" models trained on many tasks. We explore merging i…
Hierarchical Indexing for Retrieval-Augmented Opinion Summarization
Tom Hosking, Hao Tang, Mirella Lapata
We propose a method for unsupervised abstractive opinion summarization, that combines the attributability and scalability of extractive approaches with the coherence and fluency of…
Human Feedback is not Gold Standard
Tom Hosking, Phil Blunsom, Max Bartolo
Human feedback has become the de facto standard for evaluating the performance of Large Language Models, and is increasingly being used as a training objective. However, it is not…
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing
Tom Sherborne, Tom Hosking, Mirella Lapata
Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. Previous work has pri…
Hierarchical Sketch Induction for Paraphrase Generation
Tom Hosking, Hao Tang, Mirella Lapata
We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch. We introduce Hierarchical Refinement Qu…