URLs Help, Topics Guide: Understanding Metadata Utility in LLM Training
Dongyang Fan, Vinko Sabol\v{c}ec, Martin Jaggi

TL;DR
This paper systematically evaluates the impact of different metadata types, like URLs and topic information, on LLM training efficiency and controllability, revealing that URLs speed up training and topic metadata aids output steering.
Contribution
It provides a comprehensive analysis of how various metadata types influence LLM training and generation, highlighting the specific benefits of URL context and topic metadata.
Findings
URL context accelerates training
Topic and format metadata enable controllable generation
Longer prompts enhance downstream performance with URL conditioning
Abstract
Large Language Models (LLMs) are commonly pretrained on vast corpora of text without utilizing contextual metadata such as source, quality, or topic, leading to a context-free learning paradigm. While recent studies suggest that adding metadata like URL information as context (i.e., auxiliary inputs not used in the loss calculation) can improve training efficiency and downstream performance, they offer limited understanding of which types of metadata are truly effective and under what conditions. In this work, we conduct a systematic evaluation and find that not all metadata types contribute equally. Only URL context speeds up training, whereas quality scores and topic/format domain information offer no clear benefit. Furthermore, the improved downstream performances of URL conditioning emerge only when longer prompts are used at inference time. In addition, we demonstrate that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Artificial Intelligence in Healthcare and Education · Text Readability and Simplification
MethodsUmbrella Reinforcement Learning
