Multilingual prompting studies often compare English instructions with target-language instructions. For classification, however, this comparison bundles more than instruction wording: prompts also define categories, name them, bind them to answer symbols, and specify an output form. We study this label interface in Chinese-English XNLI with examples, gold labels, and decoding fixed. Under ordinary semantic-label prompting, Qwen3-8B shows large CN-interface gains on both input sides, larger Qwen3 models are near zero, and Llama3.1-70B shows a smaller gain concentrated on Chinese inputs. Removing visible category names with all six A/B/C permutations nearly eliminates the Qwen3-8B gains, while Llama-3.1-70B retains a Chinese-input residual. Restoring names inside A/B/C options improves accuracy and reduces the largest mapping instabilities, but signed contrasts remain model-specific. Direct semantic-label output adds only small gains once category names are visible. A targeted 2 × 2 follow-up associates the Llama-3.1-70B Chinese-input residual primarily with procedural-block language on average, moderated by relation-definition language; alternate-label and crossed-label checks further show that exact label wording and language can shrink or reverse the signed contrast. We use semantic label anchoring only as descriptive shorthand. The result is a scoped measurementvalidity finding for Chinese-English XNLI and the tested prompt family, not a claim of invariance across language pairs or prompt paraphrases.

We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting whether text-only Xiaohongshu (RedNote) posts elicit Upward, Downward, or Neutral/no clear social comparison from a first-person reader perspective. The task targets a socially meaningful relational, behaviorally real signal not reducible to sentiment. Across prompted LLM classifiers and supervised Chinese encoders, we find a consistent generation–detection mismatch: the signal is textually learnable in-domain, but not robustly accessible to prompt-based classification. Prompted LLM classifiers show stable failures, especially neutralization of comparison-eliciting posts and model-specific directional skew. A controlled pilot shows that LLM-generated Xiaohongshu-style posts can shift perceived standing and comparison-related affect even when prompt-based detection of the same construct remains fragile. XHS-SCoRE contributes a benchmark for reader-grounded comparison detection and a diagnostic framework for studying when socially meaningful relational cues remain only partially visible to prompt-based inference.