WordPolo: Evaluating Language Models Through Iterative Semantic Feedback
Quick summary
arXiv:2609.19006v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Large Reasoning Models (LRMs) are typically evaluated on challenging benchmarks through dataset accuracy alone, providing no insight into the quality or faithfulness of their reasoning processes. We present WordPolo, a word-finding task where participants must discover an unknown target word using semantic similarity feedback. Players start with zero knowledge, make guesses, and receive distance scores (1 = correct, higher = further away). Success requires interpreting scores to navigate semantic space and syste
Key takeaways
- arXiv:2609.19006v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Large Reasoning Models (LRMs) are typically evaluated on challenging benchmarks through dataset accuracy alone, providing no insight into the quality or faithfulness of their reasoning processes.
- We present WordPolo, a word-finding task where participants must discover an unknown target word using semantic similarity feedback.
- Players start with zero knowledge, make guesses, and receive distance scores (1 = correct, higher = further away).
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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