Papers with STD
Investigating the (De)Composition Capabilities of Large Language Models in Natural-to-Formal Language Conversion (2025.naacl-long)
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| Challenge: | Existing frameworks for evaluating the decomposition and composition capabilities of large language models (LLMs) in N2F are inadequate, and there are errors that can be attributed to deficiencies in natural language understanding and the learning and use of symbolic systems. |
| Approach: | They propose a framework that semi-automatically performs sample and task construction . main findings include that LLMs are deficient in both decomposition and composition . |
| Outcome: | The proposed framework evaluates the most advanced LLMs on a variety of common formal languages. |
Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models (2025.acl-long)
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| Challenge: | Sticky tokens, when repeatedly inserted into sentences, pull sentence similarity toward a certain value, disrupting the normal distribution of embedding distances and degrading downstream performance. |
| Approach: | They propose a method to detect “sticky tokens” by sentence and token filtering and apply it to 40 checkpoints across 14 model families. |
| Outcome: | The proposed method detects 868 sticky tokens across 14 models and shows that their presence does not correlate with model size or vocabulary size. |