Papers by Nafise Moosavi
Adaptable Adapters (2022.naacl-main)
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| Challenge: | Existing work uses the same adapter architecture for every dataset regardless of the properties of the dataset or the amount of training data. |
| Approach: | They propose to use adaptable adapters to finetune lightweight neural network layers on top of pretrained weights. |
| Outcome: | The proposed adapters achieve on-par performances with the standard adapter architecture while using a considerably smaller number of adapter layers. |
Learning From Free-Text Human Feedback – Collect New Datasets Or Extend Existing Ones? (2023.emnlp-main)
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| Challenge: | Existing datasets for learning from free-text human feedback are scarce. |
| Approach: | They manually annotate a subset of a popular dialogue dataset with error and user response types using an improved version of the Integrated Error Taxonomy and a newly proposed user response type taxonomies. |
| Outcome: | The proposed dataset provides new insights into dataset composition, error types, user response types, and the relations between them. |
LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for natural language generation tasks favor text generated by different LMs . human evaluation by experts is the most reliable approach, but it is costly and time-consuming . |
| Approach: | They examine whether language model-driven evaluation metrics exhibit bias toward underlying language models in the context of summarization tasks. |
| Outcome: | The proposed evaluation metrics tend to assign inflated scores to outputs generated by the very model they are based on. |
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization (2022.naacl-main)
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| Challenge: | Neural abstractive summarization models generate factually inconsistent summaries . previous work has introduced the task of recognizing factual inconsistency as a downstream application of natural language inference (NLI). |
| Approach: | They propose a data generation pipeline that enables a task-oriented approach to detect factual inconsistencies in abstractive summarization models. |
| Outcome: | The proposed model improves the state-of-the-art performance across four benchmarks for recognizing factual inconsistency in generated summaries. |