2 citations · 2 across the 4 of their papers we have counts for
5 papers
Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine
Aaron Fainman, Gabriela Kadlecová, Maciej Gryka +7
Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calling, and fine-tuning them nee…
Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles
Roman Neruda, Martin Bakoš, Josef Šlerka +3
Large language models (LLMs) trained on large-scale internet corpora encode extensive statistical regularities about social identities, attitudes, and political behaviour. This pap…
Where to Begin: Efficient Pretraining via Subnetwork Selection and Distillation
Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik +5
Small Language models (SLMs) offer an efficient and accessible alternative to Large Language Models (LLMs), delivering strong performance while using far fewer resources. We introd…
Transferrable Surrogates in Expressive Neural Architecture Search Spaces
Shiwen Qin, Gabriela Kadlecová, Martin Pilát +5
Neural architecture search (NAS) faces a challenge in balancing the exploration of expressive, broad search spaces that enable architectural innovation with the need for efficient…
Surprisingly Strong Performance Prediction with Neural Graph Features
Gabriela Kadlecová, Jovita Lukasik, Martin Pilát +4
Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Alt…