Cultural Awareness is Represented but Not Decoded: Tracing Mythological Knowledge across 18 Open-Source LLMs
Score
78首次将文化输出偏差归因到解码器读出阶段并提供系统性多模型证据，信息增量显著。
实验设计覆盖8个架构家族18个模型，结合多项探针技术且附带可复现资源，质量扎实。
为模型开发者提供了可干预的层与机制位置，配套工具与基准可直接用于后续改进。
当前LLM文化公平性讨论活跃，该研究以机制视角切入，及时且具启发性。
来自arXiv AI/CL/LG预印本，方法细节充分，且开放资源便于第三方验证。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
Broad model coverage, rigorous mechanistic attribution, and reproducible tooling plus benchmark make this a high-value empirical contribution to cross-cultural NLP interpretability.
AI commentary
「This paper turns the observation that 'models know but fail to answer' into a localizable computational fact. For teams deploying multilingual models, it shows the decoder readout is the real bottleneck for cultural bias; for the research community, it offers a reusable analysis framework to run per-entity probes on your own model before deciding whether to fix training or decoding.」
Quick overview
This paper probes mythological knowledge across 18 open-source LLMs from 8 architecture families, finding that residual streams cleanly distinguish cultures, yet decoders collapse culturally-specific tokens onto dominant Western traditions. Using linear probing, logit lens, activation patching, and output extraction, the authors localize the failure to the readout stage rather than representation. They also show that prompt language gates the decoder, with failures clustering within language but decoupling across languages. The release includes a per-entity decomposition framework, citation-anchored cross-cultural ground truth, and per-entity predictions for all 18 models.
- Models distinguish mythological entities across cultures internally, but outputs collapse less-represented traditions (Finnish, Slavic, Egyptian, Chinese) onto dominant Greco-Roman or Norse counterparts.
- Linear probing and activation patching show cultural information exists in residual streams, localizing the failure to decoder readout rather than representation.
- Prompt language gates decoding: failures cluster within language and decouple across languages.
- Releases include a per-entity probe/ouput decomposition framework, citation-anchored cross-cultural ground truth, and per-entity predictions for all 18 models.
Full text
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