LLMs Infer Cultural Context but Fail to Apply It When Responding D
AI benchmark · Multilingual
- Publisher
- Paper Arxiv
- Domains
- Multilingual
- Tier
- D why
- Snapshot
What is LLMs Infer Cultural Context but Fail to Apply It When Responding?
Recent work has shown that LLMs overrepresent dominant cultures, particularly Western ones, while marginalizing others. We investigate whether this affects models' ability to generate culturally adapted responses by evaluating their use of local measurement units based on the user's perceived cultural background. We introduce Cultural and Pragmatic Response Inference (CAPRI), a dataset of conversations with varying levels of cultural cues. Experiments with state-of-the-art LLMs show that models can infer cultural background and recall relevant conventions, but often fail to utilize the information to adapt their answers to the relevant cultural conventions, unless explicitly prompted to perform the tasks sequentially. We further evaluate adaptation to the interpretation of time and quantity expressions, two subjective language grounding dimensions that are affected by culture. We find that models increasingly adapt their answers as cultural cues accumulate, but their priors are not culture-neutral, sometimes aligning with the model's country of origin. Overall, CAPRI provides a resource for future research aimed at narrowing the gap between cultural knowledge and culturally adaptive language generation.
Tier D
Long tail: little or no usage data yet.
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- 16 models scored
- no proper name (a paper title or description)
- in curated RL directories (1 pt)
How we got to tier D
- Adoption: 16 models scored; no proper name (a paper title or description). Tier D.
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- In curated RL directories: 1 point, a floor of none.
- Not yet confirmed by a line from its own site or paper.
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