9 papers
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma +31
Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they a…
How Long Is a Piece of String? A Brief Empirical Analysis of Tokenizers
Jonathan Roberts, Kai Han, Samuel Albanie
Frontier LLMs are increasingly utilised across academia, society and industry. A commonly used unit for comparing models, their inputs and outputs, and estimating inference pricing…
GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models
Jonathan Roberts, Kai Han, Samuel Albanie
Large multimodal models (LMMs) have exhibited proficiencies across many visual tasks. Although numerous well-known benchmarks exist to evaluate model performance, they increasingly…
A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility
Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao +3
Reasoning has emerged as the next major frontier for language models (LMs), with rapid advances from both academic and industrial labs. However, this progress often outpaces method…
ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities
Adhiraj Ghosh, Sebastian Dziadzio, Ameya Prabhu +3
Traditional fixed test sets fall short in evaluating open-ended capabilities of foundation models. To address this, we propose ONEBench(OpeN-Ended Benchmarking), a new testing para…
A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks
Vishaal Udandarao, Mehdi Cherti, Shyamgopal Karthik +3
We investigate 17 benchmarks (e.g. SugarCREPE, VALSE) commonly used for measuring compositional understanding capabilities of vision-language models (VLMs). We scrutinize design ch…