Papers by Michelle Wastl
It’s Not What You Say, It’s How You Say It: Evaluating LLM Responses to Expressions of Belief (2026.acl-long)
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| Challenge: | a typology is grounded in four linguistically motivated dimensions: form, evidentiality, epistemic stance, and tone. |
| Approach: | They propose a typology to evaluate how different EoBs affect whether models follow context versus prior knowledge. |
| Outcome: | The proposed model systematically evaluates 16 LLMs that differ in architecture, scale, and training stages . human listeners subconsciously interpret the belief based on how it is expressed, i.e., its explicitness, tone, or contextual cues. |
Machine Translation Models are Zero-Shot Detectors of Translation Direction (2025.findings-acl)
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| Challenge: | Existing approaches to detect the translation direction of parallel text are lacking in the machine translation community. |
| Approach: | They propose an unsupervised approach to detection of translation direction of parallel texts . they use a simple hypothesis that p(translation|original)>p(original|translation) they confirm the approach is effective for high-resource language pairs . |
| Outcome: | The proposed approach achieves document-level accuracies of 82–96% for NMT-produced translations and 60–81% for human translations, based on the model used. |
SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents (2026.acl-long)
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| Challenge: | Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment . but as a standalone task, it has received little attention, a new study shows . evaluating semantic differences between documents is an underexplored challenge in natural language understanding . |
| Approach: | They introduce SwissGov-RSD, the first naturalistic, document-level, cross-lingual dataset for semantic difference recognition. |
| Outcome: | The proposed dataset shows that current approaches perform poorly on monolingual, sentence-level and synthetic benchmarks. |