Papers by Tingyu Zhang

Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration (2025.acl-long)

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Shao Zhang, Xihuai Wang, Wenhao Zhang, Chaoran Li, Junru Song, Tingyu Li, Lin Qiu, Xuezhi Cao, Xunliang Cai, Wen Yao, Weinan Zhang, Xinbing Wang, Ying Wen

Linguistic Theories

Challenge:	Large language models (LLMs) excel in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction.
Approach:	They propose a language agent framework that integrates System 1 and System 2 for efficient real-time simultaneous human-AI collaboration.
Outcome:	The proposed framework improves on existing LLM-based agents and human collaborators by integrating Theory of Mind and asynchronous reflection to infer human intentions and perform reasoning-based autonomous decisions.

A Survey of Reasoning-Intensive Retrieval: Progress and Challenges (2026.acl-long)

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Yiyang Wei, Tingyu Song, Siyue Zhang, Yilun Zhao

Reasoning

Challenge:	Reasoning-Intensive Retrieval (RIR) targets retrieval settings where relevance is mediated by latent inferential links between a query and supporting evidence, rather than semantic similarity.
Approach:	They propose a taxonomy that categorizes methods based on where and how reasoning is integrated into the retrieval pipeline.
Outcome:	The proposed method framework provides a detailed analysis of the current landscape and its trade-offs and practical applications.

Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing (2026.acl-long)

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Tingyu Song, Yanzhao Zhang, Mingxin Li, Zhuoning Guo, Dingkun Long, Pengjun Xie, Siyue Zhang, Yilun Zhao, Shu Wu

Multimodality

Challenge:	Composed Image Retrieval (CIR) is a complex task in multimodal understanding . current CIR benchmarks lack a robust evaluation pipeline and limited query categories .
Approach:	They construct a fine-grained CIR benchmark that allows for precise control over modification types and content.
Outcome:	The proposed benchmark covers 5,000 high-quality queries structured across five main categories and fifteen subcategories.

Retrieval Augmented Instruction Tuning for Open NER with Large Language Models (2025.coling-main)

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Tingyu Xie, Jian Zhang, Yan Zhang, Yuanyuan Liang, Qi Li, Hongwei Wang

Semantic Text Processing

Challenge:	Existing studies have focused on integrating large language models (LLMs) with information extraction (IE) however, the best approach to incorporate information with LLMs for IE remains an open question.
Approach:	They propose to use a Chinese IT dataset to perform RA-IT for IE . they use semantically similar examples from the training dataset as the context .
Outcome:	The proposed approach is evaluated in English and Chinese scenarios.

Specialization through Collaboration: Understanding Expert Interaction in Mixture-of-Expert Large Language Models (2026.eacl-long)

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Yuanbo Tang, Naifan Zhang, Yan Tang, Meixuan Chen, Shuhan Huang, Tingyu Cao, Yang Li

Language Models

Challenge:	Mixture-of-Experts (MoE) based large language models are popular for multitasking . however, whether each expert can specialize to a task remains unclear .
Approach:	They propose to use a dictionary learning approach to analyze expert collaboration mechanisms in MoE LLMs.
Outcome:	The proposed model outperforms existing methods by 2.5% while enabling 50% expert reduction.

Empirical Study of Zero-Shot NER with ChatGPT (2023.emnlp-main)

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Tingyu Xie, Qi Li, Jian Zhang, Yan Zhang, Zuozhu Liu, Hongwei Wang

Low-Resource NLP

Challenge:	Large language models (LLMs) have been a key component of natural language processing (NLP) .
Approach:	They propose to decompose the NER task into simpler subproblems by labels and propose a syntactic augmentation strategy to stimulate model's intermediate thinking.
Outcome:	The proposed methods achieve remarkable improvements for zero-shot NER across seven benchmarks, including Chinese and English datasets.

Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models (2024.naacl-short)

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Tingyu Xie, Qi Li, Yan Zhang, Zuozhu Liu, Hongwei Wang

Language Models

Challenge:	Existing studies exploring the performance of large language models on named entity recognition tasks have focused on training task-specific LLMs for NER.
Approach:	They propose a training-free self-improving framework that utilizes an unlabeled corpus to stimulate the self-learning ability of LLMs.
Outcome:	The proposed framework improves performance on the named entity recognition task by using an unlabeled corpus.

Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems (2026.acl-long)

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Yilun Zhao, Jinbiao Wei, Tingyu Song, Siyue Zhang, Chen Zhao, Arman Cohan

Information Retrieval

Challenge:	Existing evaluation benchmarks for retrievers are narrow and evaluate them in isolation . existing evaluation benchmarking frameworks focus on evaluating retrievers in isolation, obscuring their value in real-world applications.
Approach:	They propose an evaluation framework that evaluates retrievers in agentic search systems . they provide expert-annotated reasoning aspects, positive documents, a reference response and evaluation rubrics .
Outcome:	The proposed framework assesses retrievers in agentic search systems.

LimRank: Less is More for Reasoning-Intensive Information Reranking (2025.emnlp-main)

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Tingyu Song, Yilun Zhao, Siyue Zhang, Chen Zhao, Arman Cohan

Reasoning

Challenge:	Existing approaches to rerank information require large-scale fine-tuning, which is computationally expensive.
Approach:	They propose an open-source pipeline for generating diverse, challenging, and realistic reranking examples.
Outcome:	The proposed model performs competitively on two benchmarks, while being trained on less than 5% of the data typically used in prior work.

SELP: A Semantically-Driven Approach for Separated and Accurate Class Prototypes in Few-Shot Text Classification (2024.findings-acl)

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Wenxin Liang, Tingyu Zhang, Han Liu, Feng Zhang

Information Extraction & Text Mining

Challenge:	Existing methods for few-shot text classification focus on enhancing separation of prototypes without taking semantic relationships into account.
Approach:	They propose to utilize semantically enhanced labels to calibrate class Prototypes . they propose a center loss method to enhance intra-class compactness .
Outcome:	The proposed method outperforms baseline methods on eight few-shot text classification datasets.

KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions (2026.acl-long)

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Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, Shuo Zhang, Sen Hu, Silin Wu, Qizhen Lan, Huacan Wang, Ronghao Chen

Information Retrieval

Challenge:	Existing long-horizon memory benchmarks use multi-turn dialogues or synthetic user histories . despite rapid progress on long-term memory evaluation, there are gaps in existing benchmarks .
Approach:	They propose a long-form autobiographical narrative benchmark that reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions.
Outcome:	The proposed benchmarks build from long-form autobiographical narratives . they show that retrieval-augmented systems improve factual accuracy while errors persist on temporally grounded explanations and higher-level inferences.