Papers by Zhang Zeqi
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (2023.findings-acl)
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| Challenge: | Existing work adopts data augmentation techniques to generate pseudo-annotated sentences . existing methods neither preserve semantic consistency of original sentences nor preserve syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations. |
| Approach: | They propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. |
| Outcome: | The proposed technique can bring 2.0% F1 improvements in three datasets under low-resource setting. |
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%. |
PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)
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| Challenge: | Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling. |
| Approach: | They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling . |
| Outcome: | The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data. |
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning (2024.emnlp-main)
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| Challenge: | Existing methods for generating step-by-step rationales fail to fully utilize the relative merits of intermediate steps, limiting the effectiveness of feedback provided. |
| Approach: | They propose a tree-based preference learning verifier that constructs reasoning trees via a best-first search algorithm and collects step-level paired data for preference training. |
| Outcome: | The proposed approach outperforms existing benchmarks on arithmetic and commonsense reasoning tasks. |
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)
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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
| Challenge: | Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios. |
| Approach: | They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy. |
| Outcome: | The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em. |
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem (2022.findings-emnlp)
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| Challenge: | Existing methods for generating complex semantics and diverse equations are limited by a fixed view. |
| Approach: | They propose a multi-view consistent contrastive learning approach that decouples human reasoning into two independent but consistent views. |
| Outcome: | The proposed approach significantly outperforms existing baselines on complex problems on multiple languages. |
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. |
De-Bias for Generative Extraction in Unified NER Task (2022.acl-long)
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| Challenge: | Existing methods for Named entity recognition (NER) are not consistent with the task, which makes the model vulnerable to incorrect biases. |
| Approach: | They propose to use generative model to recognize entities from sentences . they analyze incorrect biases in the generation process from a causal perspective . |
| Outcome: | The proposed method improves the performance of the generative NER model in various datasets. |
An Expression Tree Decoding Strategy for Mathematical Equation Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to generate mathematical equations from natural language ignore parallel or dependent relations between math expressions. |
| Approach: | They propose to integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. |
| Outcome: | The proposed method outperforms baseline methods for generating mathematical equations from natural language. |
Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang
| Challenge: | Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant. |
| Approach: | They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs. |
| Outcome: | The proposed method improves performance on a range of natural language processing tasks. |
Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections. |
| Approach: | They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step . |
| Outcome: | The proposed method achieves state-of-the-art on five widely used benchmarks. |
PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
| Approach: | They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities. |
| Outcome: | The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism . |
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)
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| Challenge: | Named entity recognition (NER) is a well-studied task in natural language processing. |
| Approach: | They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them . |
| Outcome: | The proposed method outperforms state-of-the-art models on nested NER datasets. |
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 . |
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model (2024.emnlp-main)
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Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang
| Challenge: | Using large language models, large multimodal models struggle with basic tasks like reading time from a clock and planning a route using a road map. |
| Approach: | They propose a multimodal self-instruct that synthesizes massive abstract images and visual reasoning instructions. |
| Outcome: | The proposed model synthesizes 11,193 abstract images and reasoning instructions across eight visual scenarios. |