Papers by Jieyu Lin
Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)
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| Challenge: | Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say . |
| Approach: | They propose to use syntactically simple sentences to test the inference ability of NLI models. |
| Outcome: | The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair. |
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning (2021.emnlp-main)
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| Challenge: | Existing models for metaphor detection require a large amount of labeled data and are not linguistically-based. |
| Approach: | They propose a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning using a pre-trained model to obtain a contextual representation of target words. |
| Outcome: | The proposed model outperforms existing models on several benchmark datasets and achieves better performance against state-of-the-art models. |
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models (2021.acl-short)
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| Challenge: | Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. |
| Approach: | They propose a method to attack MRC models by exposing statistical biases in a RACE dataset and propose an augmented training method that can greatly reduce models’ statistical bias. |
| Outcome: | The proposed method can reduce models’ statistical biases from human-level performance to chance-level. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |