Papers by Max Ploner
Familarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data (2025.naacl-long)
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| Challenge: | Current research relies on large synthetic datasets to train zero-shot named entity recognition models. |
| Approach: | They propose a metric that captures the semantic similarity between entity types in training and evaluation to estimate label shift. |
| Outcome: | The proposed metric captures semantic similarity between entity types in training and evaluation, and their frequency in training data to provide an estimate of label shift. |
Parameter-Efficient Fine-Tuning: Is There An Optimal Subset of Parameters to Tune? (2024.findings-eacl)
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| Challenge: | Recent research has illuminated the possibility of selective parameter-efficient fine-tuning, which retains the inference speed of the original model and comes at no additional computational cost. |
| Approach: | They propose to selectively update only a small subset of parameters during the fine-tuning process, keeping the remaining parameters frozen during training. |
| Outcome: | The proposed methods retain the inference speed of the original model and come at no additional computational cost. |
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models (2024.findings-naacl)
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| Challenge: | Existing methods to evaluate LMs rely on objective function and are therefore limited to masked or causal LM types. |
| Approach: | They propose an approach that uses an LM’s inherent ability to estimate the log-likelihood of any given textual statement. |
| Outcome: | The proposed framework can probe for knowledge across different LM types. |
Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks (2024.findings-acl)
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| Challenge: | Existing models for NLP tasks require fine-tuning, but it is computationally infeasible. |
| Approach: | They propose an approach that inexpensively estimates a ranking of the expected performance of a given set of transformer language models for a specific task. |
| Outcome: | The proposed model improves the Pearson correlation coefficient between the true model ranks and the estimate. |