Papers with meaning-preserving
PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs (2026.eacl-long)
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| Challenge: | Existing evaluations of sentence embedding models rely on static tests like the Massive Text Embedding Benchmark (MTEB) repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. |
| Approach: | They propose a dynamic protocol that generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. |
| Outcome: | The proposed protocol generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. |
Content Fuzzing for Escaping Information Cocoons on Digital Social Media (2026.findings-acl)
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| Challenge: | Information cocoons restrict users’ exposure to posts with diverse viewpoints . social media platforms restrict the range of viewpoints that users encounter . |
| Approach: | They propose a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent and induces different machine-inferred stance labels. |
| Outcome: | The proposed framework rewrites posts while preserving human-interpreted intent and induces different machine-inferred stance labels while maintaining semantic integrity with respect to the original content. |