URLs Help, Topics Guide: Understanding Metadata Utility in LLM Training

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 context-aware pretraining enables more controllable generation than context-free pretraining, in a classifier-free guidance fashion. Although topic and format metadata do not accelerate training, they are effective for steering outputs, offering human-interpretable control over generation.