Papers by Bac Le

1 papers
Causal Direct Preference Optimization for Language Model Alignment (2026.findings-eacl)

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Challenge: Empirical evaluations show that CDPO surpasses DPO-based baselines by achieving unbiased fine-tuning through causal reasoning.
Approach: They propose a framework that incorporates causal inference principles to mitigate the influence of confounders and sharpen the signal of genuine human preferences.
Outcome: The proposed framework preserves the tractability of direct optimization while enhancing robustness to spurious correlations and annotation biases.

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