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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.15165 |
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| _version_ | 1866913038411497472 |
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| author | Vasileva, Lisa Sim, Karin |
| author_facet | Vasileva, Lisa Sim, Karin |
| contents | LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overgenerations are different from the neurobabble seen in NMT and range from LLM self-explanations, to risky confabulations, to appropriate explanations, where the LLM is able to act as a human translator would, enabling greater comprehension for the target audience. Detecting and determining the exact nature of the overgenerations is a challenging task. We detail different strategies we have explored for our work in a commercial setting, and present our results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15165 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fabricator or dynamic translator? Vasileva, Lisa Sim, Karin Computation and Language LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overgenerations are different from the neurobabble seen in NMT and range from LLM self-explanations, to risky confabulations, to appropriate explanations, where the LLM is able to act as a human translator would, enabling greater comprehension for the target audience. Detecting and determining the exact nature of the overgenerations is a challenging task. We detail different strategies we have explored for our work in a commercial setting, and present our results. |
| title | Fabricator or dynamic translator? |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.15165 |