Papers by Hendrik Schuff
How are Prompts Different in Terms of Sensitivity? (2024.naacl-long)
Copied to clipboard
| Challenge: | In-context learning (ICL) has become one of the most popular learning paradigms due to the rapid development of large language models (LLMs). |
| Approach: | They propose a prompt analysis based on sensitivity and introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding. |
| Outcome: | The proposed approach is particularly useful when information in the input is scarce. |
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. |
| Approach: | They propose a hierarchical model and a new regularization term to strengthen the coupling of answer and explanation and two evaluation scores to quantify the couple. |
| Outcome: | The proposed model strengthens the answer-explanation coupling and provides evaluation scores that align with user experience. |
Neighboring Words Affect Human Interpretation of Saliency Explanations (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. |
| Approach: | They conduct a user study to examine how the marking of a word’s *neighboring words* affect the explainee’s perception of the word’ s importance in the context of . a saliency explanation. |
| Outcome: | The findings question whether text-based saliency explanations should continue to be communicated at word level and inform future research on alternative methods. |
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings (2024.lrec-main)
Copied to clipboard
| Challenge: | Currently, prompt-based models are gaining popularity due to their easier adaptability in low-resource settings. |
| Approach: | They analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness and compare them with attribution score extracted from fine-tuned models and large language models. |
| Outcome: | The proposed model outperforms attention and Integrated Gradients in plausibility and faithfulness, while fine-tuning models are harder to explain in low-resource settings. |
Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing studies on sociodemographic prompting have not explored the effectiveness of this technique. |
| Approach: | They propose to use sociodemographic prompting to steer models towards answers that humans with specific sociodemography would give. |
| Outcome: | The proposed technique can improve zero-shot learning by focusing on human sociodemographic profiles. |