Can We Locate and Prevent Stereotypes in LLMs?
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866913053378871296 |
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| author | D'Souza, Alex |
| author_facet | D'Souza, Alex |
| contents | Stereotypes in large language models (LLMs) can perpetuate harmful societal biases. Despite the widespread use of models, little is known about where these biases reside in the neural network. This study investigates the internal mechanisms of GPT 2 Small and Llama 3.2 to locate stereotype related activations. We explore two approaches: identifying individual contrastive neuron activations that encode stereotypes, and detecting attention heads that contribute heavily to biased outputs. Our experiments aim to map these "bias fingerprints" and provide initial insights for mitigating stereotypes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19764 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Can We Locate and Prevent Stereotypes in LLMs? D'Souza, Alex Computation and Language Artificial Intelligence Stereotypes in large language models (LLMs) can perpetuate harmful societal biases. Despite the widespread use of models, little is known about where these biases reside in the neural network. This study investigates the internal mechanisms of GPT 2 Small and Llama 3.2 to locate stereotype related activations. We explore two approaches: identifying individual contrastive neuron activations that encode stereotypes, and detecting attention heads that contribute heavily to biased outputs. Our experiments aim to map these "bias fingerprints" and provide initial insights for mitigating stereotypes. |
| title | Can We Locate and Prevent Stereotypes in LLMs? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.19764 |