Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning

Fuente: arXiv
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Autores principales: Choenni, Rochelle, Shutova, Ekaterina
Formato: Preprint
Publicado: 2024
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author Choenni, Rochelle
Shutova, Ekaterina
author_facet Choenni, Rochelle
Shutova, Ekaterina
contents Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic. In this work, we study whether we can exploit existing knowledge about cultural values at inference time to adjust model responses to cultural value probes. We present a simple and inexpensive method that uses a combination of in-context learning (ICL) and human survey data, and show that we can improve the alignment to cultural values across 5 models that include both English-centric and multilingual LLMs. Importantly, we show that our method could prove useful in test languages other than English and can improve alignment to the cultural values that correspond to a range of culturally diverse countries.
format Preprint
id arxiv_https___arxiv_org_abs_2408_16482
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning
Choenni, Rochelle
Shutova, Ekaterina
Computation and Language
Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic. In this work, we study whether we can exploit existing knowledge about cultural values at inference time to adjust model responses to cultural value probes. We present a simple and inexpensive method that uses a combination of in-context learning (ICL) and human survey data, and show that we can improve the alignment to cultural values across 5 models that include both English-centric and multilingual LLMs. Importantly, we show that our method could prove useful in test languages other than English and can improve alignment to the cultural values that correspond to a range of culturally diverse countries.
title Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning
topic Computation and Language
url https://arxiv.org/abs/2408.16482