Papers by Uyen Le
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models (2023.eacl-main)
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| Challenge: | Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. |
| Approach: | They propose to fine-tune three state-of-the-art language models on SQuAD 1.1 or SQu AD 2.0 and then evaluate their robustness under adversarial attacks. |
| Outcome: | The proposed model is able to perform better under adversarial attacks than model fine-tuned on SQuAD 1.1 or 2.0. |
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. |
Causal Activation Steering via Sparse Mediation (2026.findings-eacl)
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| Challenge: | a sparse mediation steering approach to control language-model behavior is feasible, says a new study . existing methods that learn dense steering vectors modify thousands of activation dimensions simultaneously . |
| Approach: | They propose a sparse mediation steering approach that learns targeted behavioral interventions via regularized training. |
| Outcome: | The proposed method achieves 97-100% of dense baseline effectiveness across four tasks while using only 10-30% of activation dimensions. |