Papers by Andreas Madsen
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining (2022.findings-emnlp)
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| Challenge: | To explain NLP models, importance measures are often used to inform input tokens are important for making a prediction. |
| Approach: | They propose a faithfulness metric that masks allegedly important tokens and retrains the model. |
| Outcome: | The proposed metric is based on LSTM-attention models and RoBERTa models. |
Are self-explanations from Large Language Models faithful? (2024.findings-acl)
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| Challenge: | Instruction-tuned Large Language Models excel at many tasks and will explain their reasoning, so-called self-explanations. |
| Approach: | They propose to employ self-consistency checks to measure faithfulness to LLMs to determine if they are model-dependent and if their reasoning is convincing and wrong. |
| Outcome: | The proposed measures show that self-explanations are explanation, model, and task-dependent and should not be trusted in general. |