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Xin Alex Lin

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2 papers

2 author rows

AAAI Conference 2025 Conference Paper

GuideNER: Annotation Guidelines Are Better than Examples for In-Context Named Entity Recognition

Shizhou Huang
Bo Xu
Yang Yu
Changqun Li
Xin Alex Lin

Large language models (LLMs) demonstrate impressive performance on downstream tasks through in-context learning(ICL). However, there is a significant gap between their performance in Named Entity Recognition (NER) and in fine-tuning methods. We believe this discrepancy is due to inconsistencies in labeling definitions in NER. In addition, recent research indicates that LLMs do not learn the specific input-label mappings from the demonstrations. Therefore, we argue that using examples to implicitly capture the mapping between inputs and labels in in-context learning is not suitable for NER. Instead, it requires explicitly informing the model of the range of entities contained in the labels, such as annotation guidelines. In this paper, we propose GuideNER, which uses LLMs to summarize concise annotation guidelines as contextual information in ICL. We have conducted experiments on widely used NER datasets, and the experimental results indicate that our method can consistently and significantly outperform state-of-the-art methods, while using shorter prompts. Especially on the GENIA dataset, our model outperforms the previous state-of-the-art model by 12.63 F1 scores.

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ICLR Conference 2023 Conference Paper

HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization

Zefeng Cai
Chongyang Tao
Tao Shen 0001
Can Xu
Xiubo Geng
Xin Alex Lin
Liang He 0001
Daxin Jiang

Recently, large-scale text retrieval has made impressive progress, facilitating both information retrieval and downstream knowledge-intensive tasks (e.g., open-domain QA and dialogue). With a moderate amount of data, a neural text retriever can outperform traditional methods such as BM25 by a large step. However, while being applied to out-of-domain data, the performance of a neural retriever degrades considerably. Therefore, how to enable a retriever to perform more robustly across different domains or tasks and even show strong zero-shot transfer ability is critical for building scalable IR systems. To this end, we propose HypeR, a hyper-prompted training mechanism to enable uniform retrieval across tasks of different domains. Specifically, our approach jointly trains the query encoder with a shared prompt-based parameter pool and a prompt synthesizer that dynamically composes hyper-prompt for encoding each query from different tasks or domains. Besides, to avoid the mode collapse of prompt attention distribution for different queries, we design a contrastive prompt regularization that promotes the mode of prompt attention to be aligned and uniform. Through multi-task hyper-prompted training, our retriever can master the ability to dynamically represent different types of queries and transfer knowledge across different domains and tasks. Extensive experiments show our model attains better retrieval performance across different tasks and better zero-shot transfer ability compared with various previous methods.

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