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Austin V Huang

2 accepted papers

2024

A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis

ICLR 2024oral

Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. W…

Cited by 233Source ↗PDF ↗Scholar ↗
2023

Understanding HTML with Large Language Models

EMNLP 2023long findings

Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding – i.e., parsing the raw HTML of a webpage, with applications to automation of web-based tasks, crawling, and browser-…

Cited by 0Source ↗Scholar ↗