The benefits, risks and bounds of personalizing the alignment of large language models to individuals
Large language models (LLMs) undergo ‘alignment’ so that they better reflect human values or preferences, and are safer or more useful. However, alignment is intrinsically difficult because the hundreds of millions of people who now interact with LLMs have different preferences for language and conv...
Автори: | Kirk, HR, Vidgen, B, Röttger, P, Hale, SA |
---|---|
Формат: | Journal article |
Мова: | English |
Опубліковано: |
Springer Nature
2024
|
Схожі ресурси
Схожі ресурси
-
Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate
за авторством: Kirk, HR, та інші
Опубліковано: (2022) -
Is more data better? re-thinking the importance of efficiency in abusive language detection with transformers-based active learning
за авторством: Kirk, HR, та інші
Опубліковано: (2022) -
Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate
за авторством: Kirk, H, та інші
Опубліковано: (2021) -
Exploring large language models for ontology alignment
за авторством: He, Y, та інші
Опубліковано: (2023) -
Survey on large language models alignment research
за авторством: LIU Kunlin, та інші
Опубліковано: (2024-06-01)