Papers by Shusen Liu
2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP). |
| Approach: | They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence. |
| Outcome: | The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms. |
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension (D18-2)
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| Challenge: | Neural networks models have gained popularity due to their state-of-the-art performance but lack of interpretability hinders their deployment and refinement. |
| Approach: | They propose a visual analytic library that provides a user with a customizable visual anallytic environment. |
| Outcome: | The proposed visualization library provides an interactive environment in which the user can investigate and interrogate the relationships between input, model internals and output predictions. |
RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models (2024.findings-acl)
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| Challenge: | Traditionally, developing new language models involves fine-tuning pre-trained LMs . model merging is a cost-effective alternative to fine-timing LM models for multiple tasks . |
| Approach: | They propose an algorithm for merging fine-tuned language models without additional training. |
| Outcome: | The proposed algorithm outperforms baseline methods on multiple benchmarks. |
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods to extract novel relations do not achieve effective knowledge transfer . experimental results show that the proposed method is state-of-the-arts . |
| Approach: | They propose a Cluster-aware Pseudo-Labeling method to improve pseudo-labels quality . they firstly pre-trained the relation models with pre-defined relations to learn them . |
| Outcome: | The proposed method improves the pseudo-labels quality and transfer more knowledge for discovering novel relations. |