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UQLM: A Python Package for Uncertainty Quantification in Large Language Models
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| Published in: | Journal of Machine Learning Research |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1864030191194472457 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | Journal of Machine Learning Research |
| description | |
| discipline_display | Engineering & Technology |
| discipline_facet | Engineering & Technology |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:9425 |
| institution | FRELIP |
| journal_source_facet | Journal of Machine Learning Research |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | UQLM: A Python Package for Uncertainty Quantification in Large Language Models Computer Science & Information Science Computer Science & IT Engineering & Technology |
| sub_discipline_display | Computer Science & IT |
| sub_discipline_facet | Computer Science & IT |
| subject_display | Computer Science & Information Science Computer Science & IT Engineering & Technology Computer Science & Information Science Computer Science & IT Engineering & Technology |
| subject_facet | Computer Science & Information Science Computer Science & IT Engineering & Technology |
| title | UQLM: A Python Package for Uncertainty Quantification in Large Language Models |
| title_auth | UQLM: A Python Package for Uncertainty Quantification in Large Language Models |
| title_full | UQLM: A Python Package for Uncertainty Quantification in Large Language Models |
| title_fullStr | UQLM: A Python Package for Uncertainty Quantification in Large Language Models |
| title_full_unstemmed | UQLM: A Python Package for Uncertainty Quantification in Large Language Models |
| title_short | UQLM: A Python Package for Uncertainty Quantification in Large Language Models |
| title_sort | uqlm: a python package for uncertainty quantification in large language models |
| topic | Computer Science & Information Science Computer Science & IT Engineering & Technology |
| url | http://jmlr.org/papers/v27/25-1557.html |