7 citations · 17 across the 10 of their papers we have counts for
11 papers
Learning is Forgetting: LLM Training As Lossy Compression
Henry C. Conklin, Tom Hosking, Tan Yi-Chern +5
Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are structured. This limits our abilit…
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…
Attributable and Scalable Opinion Summarization
Tom Hosking, Hao Tang, Mirella Lapata
We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions b…
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…
Factorising Meaning and Form for Intent-Preserving Paraphrasing
Tom Hosking, Mirella Lapata
We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of trainin…
Querent Intent in Multi-Sentence Questions
Laurie Burchell, Jie Chi, Tom Hosking +2
Multi-sentence questions (MSQs) are sequences of questions connected by relations which, unlike sequences of standalone questions, need to be answered as a unit. Following Rhetoric…
Evaluating Rewards for Question Generation Models
Tom Hosking, Sebastian Riedel
Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. Models are trained using teacher forcing to…