Data Probes: A New Methodology to Understand LLM Performance
A new arXiv preprint (2605.18801) proposes developing systematic methodologies for generating synthetic sequences, called 'data probes,' to understand how specific data characteristics affect large language model (LLM) behavior across training, tuning, alignment, and in-context learning. The authors argue that current empirical approaches to data filtering and dataset construction are compute-intensive and lack principled understanding. By observing LLM behavior on data probes derived from random processes, researchers aim to reveal useful characteristics and fundamentally understand data's role in LLM performance.
Key facts
- arXiv preprint 2605.18801
- Proposes 'data probes' as synthetic sequences from random processes
- Aims to understand data characteristics affecting LLM behavior
- Covers training, tuning, alignment, and in-context learning stages
- Criticizes current empirical heuristics as compute-intensive
- Advocates for principled methodology over extensive experimentation
- Position paper type
- Published on arXiv
Entities
Institutions
- arXiv