Papers by Kewen Zhao
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for fine-tuning pre-trained language models are limited . we propose a few-shot fine-uning framework for NER . |
| Approach: | They propose a few-shot fine-tuning framework for named entity recognition (NER) they propose three new types of tokens, "is-entity", "which-type" and "bracket" |
| Outcome: | The proposed framework improves on pre-trained language models on several benchmark datasets. |
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)
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| Challenge: | a new framework for patent approval prediction is proposed to address this problem . novelty scores are based on comparing an application with millions of prior arts . |
| Approach: | They propose a framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
| Outcome: | The proposed framework unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph (2024.acl-long)
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| Challenge: | Scaling up language models has demonstrated predictable improvement and unprecedented abilities in many language tasks. |
| Approach: | They propose a fine-grained cLAim depeNdency graph that captures the dependencies within the patent data and extends the embedding-based state-of-the-art (SOTA) they then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. |
| Outcome: | The proposed graph methods outperform the standard model scaling methods in the patent approval prediction task and show that they are cost-effective. |