Papers with robust strategy
DaN+: Danish Nested Named Entities and Lexical Normalization (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a task of finding entities in text, such as locations, organizations, and persons. |
| Approach: | They propose a multi-domain corpus and annotation guidelines for Danish nested named entities and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. |
| Outcome: | The proposed model outperforms existing models on the Danish Named Entity Recognition task and shows that it is robust to domain shifts and is highly effective on the least canonical data. |
Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination (2026.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) are capable of processing visual inputs, but are susceptible to hallucinations. |
| Approach: | They propose a method to localize and localize specific visual tokens, which are defined as **Inert Tokens**, across layers, revealing a rigid semantic collapse. |
| Outcome: | The proposed approach reduces the likelihood of LVLMs being hijacked by visual inputs while maintaining general capabilities. |