Summary of Attributing Culture-conditioned Generations to Pretraining Corpora, by Huihan Li et al.
Attributing Culture-Conditioned Generations to Pretraining Corpora
by Huihan Li, Arnav Goel, Keyu He, Xiang Ren
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates how large language models develop cultural biases during open-ended generative tasks like narrative writing or dialogue. The authors argue that these biases may stem from uneven cultural representation in pretraining corpora, leading to limited knowledge and templated outputs for less prevalent cultures. The proposed MEMOed framework is designed to determine whether a generation for a culture arises from memorization, rather than genuine understanding. By analyzing how models associate entities with cultures based on pretraining data patterns, the authors find that high-frequency cultures in pretraining data yield more generations with memorized symbols, while some low-frequency cultures produce none. Additionally, the model favors generating entities with extraordinarily high frequency regardless of the conditioned culture, reflecting biases toward frequent pretraining terms irrespective of relevance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be biased when writing stories or dialogues because they don’t have enough information about different cultures. This bias might come from the way the models were trained on a lot of text data. The researchers wanted to see how the models developed cultural biases by looking at how they associated entities with cultures based on the training data. They created a new tool called MEMOed that helps figure out if a model is just memorizing information or actually understanding it. By using this tool, they found that when a culture appears frequently in the training data, the model will generate more templated responses related to that culture. The researchers hope their findings will help others work on fixing these biases and making language models more accurate. |
Keywords
» Artificial intelligence » Pretraining
