Papers by Katharina Von Der Wense
Large Language Models Are Overconfident in Their Own Responses (2026.findings-acl)
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| Challenge: | Prior work has shown that instruction-tuned large language models are less well calibrated than their base pre-trained counterparts. |
| Approach: | They propose a simple inference-time strategy that frams the model’s answer as user input during confidence elicitation. |
| Outcome: | The proposed approach reduces overconfidence and improves calibration by up to 26% without retraining. |
From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation (2026.findings-acl)
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| Challenge: | Existing methods to evaluate code generation bias focus on overt discrimination through simple conditional statements. |
| Approach: | They examine ML pipelines that exhibit substantially greater bias than simple conditionals . they challenge simple conditional statements as valid proxies for bias evaluation . |
| Outcome: | The proposed model underestimates real-world bias in generating machine learning pipelines . the model maintains equal performance on simple conditionals and ML pipelines, the study shows . |