Papers by Tingyu Xie
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
| 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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| 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. |
Empirical Study of Zero-Shot NER with ChatGPT (2023.emnlp-main)
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| 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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| 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. |
A Class-Rebalancing Self-Training Framework for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Distant supervision reduces the reliance on human annotation in named entity recognition tasks. |
| Approach: | They propose a class-rebalancing self-training framework for improving distantly-supervised named entity recognition by using a flexible threshold and a hybrid pseudo label. |
| Outcome: | The proposed model achieves state-of-the-art on five flat and two nested datasets and compares with other methods on the same dataset. |