Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
A new position paper advocates for the development of data probes to better understand how data influences large language model (LLM) performance. The authors argue that current methods rely too heavily on empirical heuristics and lack a systematic approach. By generating synthetic sequences, researchers can gain insights into the characteristics that affect LLM behavior during various stages of the workflow.
- ▪Data is fundamental to the performance of large language models (LLMs).
- ▪Current approaches to understanding data's impact on LLMs are compute intensive and lack principled methodologies.
- ▪The proposed data probes aim to reveal useful characteristics of data that influence model performance, generalization, and robustness.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18801 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.18801 (cs) [Submitted on 11 May 2026] Title:Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance Authors:Shiqiang Wang, Herbert Woisetschläger, Hans Arno Jacobsen, Mingyue Ji View a PDF of the paper titled Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance, by Shiqiang Wang and 3 other authors View PDF HTML (experimental) Abstract:Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, including training, tuning, alignment, in-context learning, etc., and why, remains an open question.
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