New methods enable language models to learn complex graph search tasks at scale

Researchers have identified two key factors hindering language models' ability to learn latent multi-hop graph search at scale: the supervision signal and tokenization methods. By implementing multi-token prediction (MTP) and alternative tokenizations, they have overcome previous scaling limitations. These advancements allow models to consistently and stably solve the task at significantly larger scales than previously possible. AI

IMPACT Enables more complex reasoning and problem-solving in language models for graph-based tasks.

RANK_REASON Research paper detailing novel methods for improving language model performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• Alternative Tokenizations
• Arvid Frydenlund
• arXiv
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Latent Multi-Hop Graph Search
• Multi Token Prediction
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →