Papers by Zeqi Lin
Making Language Models Better Reasoners with Step-Aware Verifier (2023.acl-long)
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| Challenge: | Large language models have made impressive progress in few-shot learning but still face difficulties in reasoning tasks such as GSM8K. |
| Approach: | They propose a new approach that uses a verifier to filter out incorrect answers based on a weighted voting scheme to improve reasoning ability of language models. |
| Outcome: | The proposed approach improves GSM8K reasoning rate by 17.9% to 58.1%. |
Reasoning Like Program Executors (2022.emnlp-main)
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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen
| Challenge: | Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs . |
| Approach: | They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors. |
| Outcome: | The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database . |
Can LLMs Learn From Mistakes? An Empirical Study on Reasoning Tasks (2024.findings-emnlp)
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| Challenge: | Existing work has shown that simple learning can enhance the chain-of-thought (CoT) reasoning of large language models. |
| Approach: | They construct mistake-correction datasets to identify and correct mistakes in CoTs . they conclude that LLMs can learn from mistakes to enhance their CoT reasoning . |
| Outcome: | The proposed datasets show that LLMs can learn from mistakes to enhance their CoT reasoning performance. |
When Language Model Meets Private Library (2022.findings-emnlp)
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| Challenge: | Existing language models have been pre-trained on large-scale code corpora and generate decent code snippets. |
| Approach: | They propose a framework that can provide pre-trained language models with the ability to generate code using private libraries. |
| Outcome: | The proposed framework can generate code using private libraries using off-the-shelf language models or pre-trained models on code corpus containing API information. |
Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)
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Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang
| Challenge: | Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. |
| Approach: | They propose an end-to-end neural model to learn algebraic recombination for compositional generalization. |
| Outcome: | The proposed model is based on two realistic and comprehensive compositional generalization benchmarks. |
Skill-Based Few-Shot Selection for In-Context Learning (2023.emnlp-main)
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| Challenge: | Existing methods based on pre-trained embeddings can be easily biased by surface features that are not important for the target task. |
| Approach: | They propose a skill-based few-shot selection method for in-context learning . it generates skill-specific descriptions for each test case and candidate example . |
| Outcome: | The proposed method significantly outperforms existing methods in five cross-domain semantic parsing datasets and six backbone models. |
STAND-Guard: A Small Task-Adaptive Content Moderation Model (2025.coling-industry)
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| Challenge: | Content moderation is important for developing welcoming online platforms and responsible large language models. |
| Approach: | They propose a small task-adaptive coNtent moDeration model that can be easily adapted to new or customized content moderation tasks without extensive model tuning. |
| Outcome: | The proposed model is comparable to GPT-3.5-Turbo on unseen English binary classification tasks. |
How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)
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| Challenge: | In-context learning paradigms that focus on large corpus are limiting compositional generalization performance. |
| Approach: | They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case . |
| Outcome: | The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples . |