EMNLP paper: Prompt2Box uncovers entailment structure to find LLM weaknesses
windx0303 · x · 2026-09-12
An EMNLP paper, Prompt2Box, argues that entailment and NLI are a crucial missing piece in how we analyze LLM performance.
- Problem: The usual approach embeds prompts into a vector space and clusters them, but vector embeddings mostly capture topical similarity — prompts sharing a topic but differing in specificity (and difficulty) end up nearly identical, blocking fine-grained weakness analysis.
- Method: A trained encoder embeds prompts into a box embedding space that captures both semantic similarity and specificity relations (e.g., "write an adventure story" is more specific than "write a story"), plus a novel dimension-reduction technique for box embeddings for visualization.
- Results: 45% average error reduction in predicting specificity vs. the prompt-length baseline; hierarchical clustering trees built for 17 LLMs on UltraFeedback.
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