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Aspect-wise Pairwise Tournaments for Test-Time Selection

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Briefings

A new method called Aspect-wise Pairwise Tournaments (APT) improves test-time LLM selection by comparing candidates aspect-by-aspect rather than judging them in isolation. The study tested APT across five domains and three different LLM generators, showing it outperforms existing methods like binary multi-aspect approval and self-consistency. The approach uses natural language aspect descriptions to guide selection, allowing users to steer choices toward specific properties without regenerating solutions.

Key points

• APT improves test-time LLM selection by comparing candidates aspect-by-aspect using off-the-shelf aspect verifiers, outperforming binary multi-aspect approval and self-consistency methods.
• The method was evaluated across five domains and three different LLM generators, demonstrating that neither binary approval nor a generic pairwise judge matches APT's performance.

Context

This research addresses scaling test-time compute for LLMs, specifically improving test-time selection methods. The study compares APT against existing approaches like binary multi-aspect approval and self-consistency, finding that APT improves performance across multiple domains.

Limitations

The study was conducted on five domains and three LLM generators, limiting generalizability. No component was trained, so APT transfers to new domains by simply rewriting prompts. Results are based on a single preprint version.

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