Papers by Jingyuan Wen
Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing work on pretraining models for text classification uses image encoders instead of visual prompts. |
| Approach: | They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning. |
| Outcome: | The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets. |
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Existing evaluation protocols for large language models (LLMs) are inadequate for conversational recommender systems. |
| Approach: | They propose an evaluation approach based on LLMs that harnesses LLM-based user simulators to evaluate ChatGPT's performance. |
| Outcome: | The proposed evaluation approach can simulate various system-user interaction scenarios. |
The Web Can Be Your Oyster for Improving Language Models (2023.findings-acl)
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| Challenge: | Pretrained language models encode a large amount of knowledge, but knowledge is frozen at the time of training, and the models become static and limited by training data. |
| Approach: | They propose an adaptive search engine assisted learning method that can self-evaluate the confidence level of PLM’s predictions and adaptively determine when to refer to the web for more data. |
| Outcome: | The proposed model outperforms retrieval-augmented methods on 16 knowledge-intensive tasks on a wide range of knowledge-related tasks. |
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment (2024.emnlp-main)
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| Challenge: | Experimental results show that large language models are struggling to align with human preference in complex tasks and scenarios. |
| Approach: | They propose a low-redundant alignment method that selects the top-10% most updated parameters in LLMs for alignment training. |
| Outcome: | The proposed method improves on 10 datasets and shows that it is redundant . it can be used to train LLMs on QA and ECQA datasets, but it is not feasible to test it on a large dataset. |
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)
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Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen
| Challenge: | Recent years have featured a trend towards Transformer based pretrained language models (PLMs) in natural language processing systems. |
| Approach: | They propose to use four evaluation dimensions to evaluate ten widely-used PLMs . they find that pretrained language models are good at different ability tests . |
| Outcome: | The results show that pretrained language models are good at different ability tests and have excellent transferability between tasks. |