Papers by Yichun Liu
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification (2025.acl-long)
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Chengwu Liu, Ye Yuan, Yichun Yin, Yan Xu, Xin Xu, Zaoyu Chen, Yasheng Wang, Lifeng Shang, Qun Liu, Ming Zhang
| Challenge: | Chain-of-Thought prompting is a de facto method to elicit reasoning capabilities from large language models (LLMs). |
| Approach: | They propose a step-aware formal verification framework Safe to address hallucinations in CoT prompting . they propose 'formal step' as a benchmark for step correctness theorem proving with 30,809 formal statements. |
| Outcome: | The proposed framework shows significant performance improvement while offering interpretable and verifiable evidence. |
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)
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| Challenge: | Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance. |
| Approach: | They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
| Outcome: | The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models (2023.acl-short)
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Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Existing research on information-seeking conversations is stymied by the lack of training data. |
| Approach: | They propose to use autoconv for synthetic conversation generation to capture the characteristics of the information-seeking process and fine tune an LLM with a few human conversations to generate synthetic conversations with high quality. |
| Outcome: | The proposed model improves on two commonly-used datasets and alleviates the dependence on human annotation. |
One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems (2023.acl-long)
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| Challenge: | Recent studies have found that Task-oriented Dialogue systems can be more suitable for human users. |
| Approach: | They propose a framework to optimize ToD systems by leveraging Multiple User SimulaTors. |
| Outcome: | The proposed framework improves performance on multiWOZ with human evaluations and automatic evaluations. |
TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for named entity recognition are limited by noise in translation . Existing approaches to named entities recognition are mainly based on labeled data . |
| Approach: | They propose a framework to mitigate lexical and syntactic errors of translated data . they propose to use multi-level adversarial learning and multi-model knowledge distillation to mitigate noise . |
| Outcome: | The proposed framework mitigates lexical and syntactic errors of translated data . it achieves competitive performance to state-of-the-art models . |
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)
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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu
| Challenge: | Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning. |
| Approach: | They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
| Outcome: | The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)
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Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang
| Challenge: | Recent advances in neural theorem-proving resort to large language models and tree searches. |
| Approach: | They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate. |
Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)
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| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |
FPT: Improving Prompt Tuning Efficiency via Progressive Training (2022.findings-emnlp)
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| Challenge: | Recent prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). |
| Approach: | They propose a prompt tuning algorithm that uses a small-scale partial PLM and progressively expands its depth and width until the full-model size. |
| Outcome: | The proposed method could save over 30% of training computations while achieving comparable performance. |
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing methods are poor at detecting complicated relations between aspects and opinions . detecting unclear boundaries of multi-word aspects and opinion is also a challenge . |
| Approach: | They propose a multi-task dual-tree network to extract triplets from a given sentence . they employ a constituency tree and a modified dependency tree to enhance the interaction . |
| Outcome: | The proposed model extracts triplets from a given sentence, and it is effective on four datasets. |
TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020.emnlp-main)
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| Challenge: | Transformer-based pre-training models like BERT are computationally expensive and limited to resource-constrained devices. |
| Approach: | They propose a method which ternarizes the weights in a fine-tuned BERT model. |
| Outcome: | The proposed method outperforms the other methods on the GLUE and SQUAD benchmarks while being 14.9x smaller. |
Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) aims to extract relations among entity pairs in documents. |
| Approach: | They propose a logic constraint framework that uses bidirectional constraints to model rules by beta contribtion and reconstruct rule consistency loss by bidirectional constraint. |
| Outcome: | The proposed framework outperforms existing models in relation extraction performance and logical consistency. |
bert2BERT: Towards Reusable Pretrained Language Models (2022.acl-long)
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Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu
| Challenge: | Pre-training large language models can be expensive and wasteful. |
| Approach: | They propose a method which can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and a two-stage learning method to further accelerate the pre-training. |
| Outcome: | The proposed method can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and significantly improve the pre-training efficiency of the large model. |
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 (2026.acl-long)
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Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang
| Challenge: | Existing approaches to solving mathematical problems fall into two broad categories: informal methods and formal methods. |
| Approach: | They propose to use LLM natural-language reasoning to discover answers . they introduce Discover And Prove framework that rewrites Hard Mode statements into Easy Mode ones for existing ATP provers. |
| Outcome: | The proposed framework can be used to prove hard mode statements on ATP benchmarks. |
TinyBERT: Distilling BERT for Natural Language Understanding (2020.findings-emnlp)
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| Challenge: | Pre-trained language models are computationally expensive and difficult to efficiently execute on resource-restricted devices. |
| Approach: | They propose a Transformer distillation method that performs Transformer distillations at pre-training and task-specific learning stages. |
| Outcome: | The proposed method accelerates inference and reduces model size while maintaining accuracy. |
NewsDialogues: Towards Proactive News Grounded Conversation (2023.findings-acl)
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Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Hot news is one of the most popular topics in daily conversations. |
| Approach: | They propose a task where a dialogue system can lead the conversation based on key topics of the news. |
| Outcome: | The proposed method can lead conversations based on key topics of the news . it can also be used in information-seeking and chit-chat scenarios . |
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models (2021.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have achieved great success in natural language processing. |
| Approach: | They propose a method that automatically searches architecture hyper-parameters in BERT . they use one-shot learning and the search space to provide an adaptive development way . |
| Outcome: | The proposed method outperforms both the baseline and distillation-based methods on GLUE and SQUAD benchmarks. |