  We propose the problem of conversational web navigation, where a digital
agent controls a web browser and follows user instructions to solve real-world
tasks in a multi-turn dialogue fashion. To support this problem, we introduce
WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert
demonstrations of conversational web navigation. Our benchmark covers a broad
range of patterns on over 150 real-world websites and can be used to train and
evaluate agents in diverse scenarios. Due to the magnitude of information
present, Large Language Models (LLMs) cannot process entire web pages in
real-time. To solve this bottleneck, we design a retrieval-inspired model that
efficiently prunes HTML pages by ranking relevant elements. We use the selected
elements, along with screenshots and action history, to assess a variety of
models for their ability to replicate human behavior when navigating the web.
Our experiments span from small text-only to proprietary multimodal LLMs. We
find that smaller finetuned decoders surpass the best zero-shot LLMs (including
GPT-4V), but also larger finetuned multimodal models which were explicitly
pretrained on screenshots. However, all finetuned models struggle to generalize
to unseen websites. Our findings highlight the need for large multimodal models
that can generalize to novel settings. Our code, data and models are available
for research: https://mcgill-nlp.github.io/weblinx
