REVIEW 6 cited by
Understanding HTML with Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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-assisted retrieval -- have not been fully explored. We contribute HTML understanding models (fine-tuned LLMs) and an in-depth analysis of their capabilities under three tasks: (i) Semantic Classification of HTML elements, (ii) Description Generation for HTML inputs, and (iii) Autonomous Web Navigation of HTML pages. While previous work has developed dedicated architectures and training procedures for HTML understanding, we show that LLMs pretrained on standard natural language corpora transfer remarkably well to HTML understanding tasks. For instance, fine-tuned LLMs are 12% more accurate at semantic classification compared to models trained exclusively on the task dataset. Moreover, when fine-tuned on data from the MiniWoB benchmark, LLMs successfully complete 50% more tasks using 192x less data compared to the previous best supervised model. Out of the LLMs we evaluate, we show evidence that T5-based models are ideal due to their bidirectional encoder-decoder architecture. To promote further research on LLMs for HTML understanding, we create and open-source a large-scale HTML dataset distilled and auto-labeled from CommonCrawl.
Forward citations
Cited by 6 Pith papers
-
Automated Test Transfer Across Android Apps Using Large Language Models
LLMigrate uses a multimodal LLM to abstract a source app test into natural language and then dynamically explores the target app to rebuild the test, reaching a 97.5% success rate over 120 transfers.
-
Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
Persona2Web is a new open-web benchmark where agents must infer a user's preferences from synthetic browsing history to solve intentionally ambiguous queries; current best agents score 13% success.
-
WebLists: Extracting Structured Information From Complex Interactive Websites Using Executable LLM Agents
BardeenAgent records a single extraction on a webpage, generalizes it with CSS selectors, and replays it across all matching items, reaching 66% recall on a new 200-task WebLists benchmark versus 31% for the best baseline.
-
CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings
CodeElo benchmarks LLMs by submitting their solutions to CodeForces's official judge and converting results into human-comparable Elo ratings, with o1-mini at 1578 and most open models below the 20th human percentile.
-
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data
TEDUO learns instruction-following policies from unlabeled offline transitions by combining LLM-generated reward labels, per-goal offline Q-learning, and supervised fine-tuning of a base LLM, enabling zero-shot genera...
-
Digi-Q: Learning Q-Value Functions for Training Device-Control Agents
An offline RL method learns a Q-function from frozen VLM features and extracts a device-control policy by imitating the best of several actions ranked by that Q-function.
Discussion (0). Continue with ORCID to comment.