Data-Centric Survey Reframes LLM Alignment as Pipeline Design
A new survey from arXiv reframes alignment tuning for large language models as a pipeline design problem, adopting a data-centric perspective. The authors decompose alignment data construction into three stages: response synthesis, preference evaluation, and preference instantiation. They organize existing methods into a unified taxonomy, identify recurring design trade-offs and failure modes, and distill high-level principles linking pipeline choices to optimization signals. Open challenges include prompt-level alignment, agentic settings, and alignment under evolving objectives.
Key facts
- Survey adopts a data-centric lens on alignment tuning for LLMs.
- Alignment data construction is decomposed into three stages: response synthesis, preference evaluation, and preference instantiation.
- Existing alignment methods are organized into a unified taxonomy.
- Recurring design trade-offs and failure modes are identified.
- High-level principles clarify how pipeline design choices influence optimization signals.
- Open challenges include prompt-level alignment, agentic settings, and alignment under evolving objectives.
- The paper is categorized under Computer Science > Computation and Language.
- The survey is available on arXiv with ID 2605.26442.
Entities
Institutions
- arXiv