Papers by Yilong Xu
ALiiCE: Evaluating Positional Fine-grained Citation Generation (2025.naacl-long)
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| Challenge: | Existing research on citation generation is limited to sentence-level statements . positional fine-grained citations can appear anywhere within sentences . |
| Approach: | They propose a framework that allows LLMs to generate citations from sentences . they use dependency tree-based methods to parse sentence-level claims into atomic claims . |
| Outcome: | The proposed framework evaluates citation quality using three metrics including positional fine-grained citation recall, precision, and coefficient of variation of citation positions. |
Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models (2025.emnlp-main)
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| Challenge: | Utility-based retrieval has emerged as a promising topic for downstream tasks . however, capturing passage utility accurately remains unexplored due to insufficient understanding . |
| Approach: | They propose a framework for training utility-based retrievers in Retrieval-Augmented Language Models . it incorporates multi-task generalization and inter-passage interaction to improve performance . |
| Outcome: | The proposed framework improves performance on ten datasets across different tasks. |
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training (2024.emnlp-main)
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| Challenge: | Emojis have gained immense popularity on social media platforms, serving as a common means to supplement or replace text. |
| Approach: | They propose a graph pre-train framework for text and emoji co-modeling that incorporates two tasks: node-level graph contrastive learning and edge-level link reconstruction learning. |
| Outcome: | The proposed framework improves on the Xiaohongshu and Twitter datasets with two types of downstream tasks. |
Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities (2024.findings-emnlp)
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| Challenge: | Existing methods to update parametric knowledge of large language models (LLMs) are outdated and incontext editing (KE) is not effective due to the substantial cost associated with retraining. |
| Approach: | They propose a new decoding technique that enhances in-context editing (ICE) they propose to use parametric knowledge to update the models' knowledge . |
| Outcome: | The proposed technique improves ICE performance while incurring only half the latency. |