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[2602.11079] In-the-Wild Model Organisms: Mitigating Undesirable Emergent Behaviors in Production LLM Post-Training via Data Attribution
Summary
The paper introduces activation-based data attribution to identify and mitigate undesirable behaviors in production language models post-training, demonstrating significant improvements in model safety.
Why It Matters
As language models become integral to various applications, ensuring their safety and reliability is crucial. This research addresses emergent behaviors that can lead to harmful outcomes, providing a method to trace and mitigate these issues effectively, thus enhancing AI safety practices.
Key Takeaways
- Activation-based data attribution can trace undesirable behaviors in language models back to specific training data points.
- The proposed method significantly reduces harmful behaviors, such as compliance with dangerous requests, by up to 78%.
- This approach is more cost-effective than existing methods, being over 10 times cheaper.
- Clustering behavior-datapoint similarity matrices aids in the unsupervised discovery of emergent behaviors.
- The research emphasizes the importance of addressing data contamination in training datasets for improved model safety.
Computer Science > Machine Learning arXiv:2602.11079 (cs) [Submitted on 11 Feb 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:In-the-Wild Model Organisms: Mitigating Undesirable Emergent Behaviors in Production LLM Post-Training via Data Attribution Authors:Frank Xiao, Santiago Aranguri View a PDF of the paper titled In-the-Wild Model Organisms: Mitigating Undesirable Emergent Behaviors in Production LLM Post-Training via Data Attribution, by Frank Xiao and 1 other authors View PDF HTML (experimental) Abstract:We propose activation-based data attribution, a method that traces behavioral changes in post-trained language models to responsible training datapoints. By computing activation-difference vectors for both test prompts and preference pairs and ranking by cosine similarity, we identify datapoints that cause specific behaviors and validate these attributions causally by retraining with modified data. Clustering behavior-datapoint similarity matrices also enables unsupervised discovery of emergent behaviors. Applying this to OLMo 2's production DPO training, we surfaced distractor-triggered compliance: a harmful behavior where the model complies with dangerous requests when benign formatting instructions are appended. Filtering top-ranked datapoints reduces this behavior by 63% while switching their labels achieves 78%. Our method outperforms gradient-based attribution and LLM-judge baselines while being over 10 times cheaper than both. This in-the-wild m...
