Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training
📰 ArXiv cs.AI
Learn to identify and mitigate undesirable behaviors in LLMs using probe-based data attribution, a method that traces behavioral changes to responsible training datapoints.
Action Steps
- Compute activation-difference vectors for test prompts and preference pairs using cosine similarity
- Rank datapoints by cosine similarity to identify those causing specific behaviors
- Retrain the model with modified data to validate attributions causally
- Cluster behavior-datapoint similarity matrices to identify patterns and trends
- Apply probe-based data attribution to mitigate undesirable behaviors in LLM post-training
Who Needs to Know This
NLP engineers and researchers can use this method to improve the reliability and transparency of their LLMs, while data scientists can apply it to identify and address biases in their models.
Key Insight
💡 Probe-based data attribution can help identify and address biases in LLMs by tracing behavioral changes to responsible training datapoints.
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🚀 Discover and mitigate undesirable behaviors in LLMs with probe-based data attribution! 🤖
Key Takeaways
Learn to identify and mitigate undesirable behaviors in LLMs using probe-based data attribution, a method that traces behavioral changes to responsible training datapoints.
Full Article
Title: Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training
Abstract:
arXiv:2602.11079v3 Announce Type: replace-cross Abstract: We propose probe-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 matr
Abstract:
arXiv:2602.11079v3 Announce Type: replace-cross Abstract: We propose probe-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 matr
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