Papers by Oliver Eberle
Rather a Nurse than a Physician - Contrastive Explanations under Investigation (2023.emnlp-main)
Copied to clipboard
| Challenge: | a recent study suggests that contrastive explanations are closer to how humans explain a decision than non-contrastive explanations. |
| Approach: | They analyze four English text-classification datasets to determine whether humans explain in contrast to alternatives. |
| Outcome: | The proposed explanations are closer to how humans explain a decision than non-contrastive explanations. |
Explaining Text Similarity in Transformer Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Modern foundation models provide flexible text representations that enable the detection of semantic structure in vast amounts of unlabeled data. |
| Approach: | They propose to leverage layer-wise relevance propagation to understand the inner prediction mechanisms of NLP models by analyzing grammatical interactions, multilingual semantics, and biomedical text retrieval. |
| Outcome: | The proposed methods demonstrate their utility in three corpus-level use cases, analyzing grammatical interactions, multilingual semantics, and biomedical text retrieval. |
Trick or Neat: Adversarial Ambiguity and Language Model Evaluation (2025.findings-acl)
Copied to clipboard
| Challenge: | Direct prompting fails to detect ambiguity while linear probes can decode ambiguities with high accuracy, sometimes exceeding 90%. |
| Approach: | They introduce an adversarial ambiguity dataset that includes syntactic, lexical, and phonological ambiguities along with adversarials. |
| Outcome: | The proposed dataset includes syntactic, lexical, and phonological ambiguities along with adversarial variations. |
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze? (2022.acl-long)
Copied to clipboard
| Challenge: | We compare attention functions in pre-trained language models to human eye fixation patterns during task-specific reading tasks. |
| Approach: | They compare attention functions in large-scale pre-trained language models to classical cognitive models of human attention by using a dataset with eye-tracking recordings of native speakers of English. |
| Outcome: | The proposed model is as predictive of human eye fixation patterns as classical cognitive models of human attention. |
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations (2024.lrec-main)
Copied to clipboard
| Challenge: | We compare webcam-based eye-tracking recordings with human-annotated rationales to evaluate importance scores. |
| Approach: | They compare webcam-based eye-tracking recordings with attention-based importance scores for 4 different multilingual Transformer-based language models. |
| Outcome: | The proposed method is comparable to human rationales in linguistic analysis. |