Papers by Yilin Zhao
Lite Unified Modeling for Discriminative Reading Comprehension (2022.acl-long)
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| Challenge: | generative and discriminative MRCs focus on answer generation, extractive MRC on answer extraction. |
| Approach: | They propose a lightweight POS-Enhanced Iterative Co-Attention Network to handle diverse discriminative MRC tasks synchronously. |
| Outcome: | The proposed model improves on four discriminative MRC benchmarks. |
DocOIE: A Document-level Context-Aware Dataset for OpenIE (2021.findings-acl)
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| Challenge: | Existing solutions focus on extracting tuples at sentence level, but sentences exist as part of a document rather than standalone. |
| Approach: | They propose to annotate 800 sentences from 80 documents to form a DocOIE dataset . they propose to use document-level context to improve OpenIE performance . |
| Outcome: | The proposed OpenIE model improves performance by incorporating documentlevel context into the dataset. |
Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm (2026.findings-acl)
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| Challenge: | Spec-Coding, a framework for coding with vague intents, is a critical step toward repository-scale autonomous engineering. |
| Approach: | They propose a structured symbolic paradigm that bridges unstructured intent and executable code via Autonomous Symbolic Grounding. |
| Outcome: | The proposed framework achieves 47% functional success while maintaining near-perfect structural integrity. |
An Efficient Context-Dependent Memory Framework for LLM-Centric Agents (2025.naacl-industry)
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| Challenge: | a recent study has demonstrated that context-dependent memory encoding can help to retrieve key memory cues essential for problem-solving. |
| Approach: | They propose an efficient architecture miming human memory processes through multistage encoding, context-aware storage, and retrieval strategies for LLM-centric agents. |
| Outcome: | The proposed architecture surpasses state-of-the-art online LLM-centric approaches on two interactive decision-making benchmarks in the navigation and manipulation domain. |
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)
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Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao
| Challenge: | Existing models lack the ability to adhere to instructions, resulting in suboptimal performance. |
| Approach: | They propose an automated iterative instruction-following benchmark with integrated feedback mechanism. |
| Outcome: | The proposed benchmark identifies erroneous components in model responses and provides feedback accurately. |
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing continual learning approaches suffer from low accuracy and performance fluctuation when the distributions of old and new data are significantly different. |
| Approach: | They propose a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. |
| Outcome: | The proposed model outperforms the best state-of-the-art method by 20% in average accuracy and each component contributes effectively to overall performance. |
TARA: Token-level Attribute Relation Adaptation for Multi-Attribute Controllable Text Generation (2024.findings-emnlp)
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| Challenge: | Existing work on multi-attribute controllable text generation ignores interrelations of attributes . recent work defines attribute relations as promotive, but not fixed . |
| Approach: | They propose a method that explicitly defines attribute relations as inhibtory for multi-attribute CTG . they propose 'tara' which employs token-level attribute relation adaptation and representation to generate text with the balanced multi-attribut . |
| Outcome: | The proposed method generates text with the balanced multi-attribute control. |
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree (2025.findings-emnlp)
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| Challenge: | Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code. |
| Approach: | They propose a structure-aware method that breaks large AST nodes into smaller chunks . this method generates self-contained, semantically coherent units across programming languages . |
| Outcome: | The proposed method boosts Recall@5 by 4.3 points on RepoEval retrieval and Pass@1 by 2.67 points on SWE-bench generation. |