Papers by Yilun Zhu
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context (2026.acl-long)
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Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Mohsen Bayati, Bryan Wang, Shervin Malmasi
| Challenge: | Existing methods for text regression lack local grounding and rely on shared representations. |
| Approach: | They propose a distributional regression model with quantile tokens that insert dedicated quantiles into the input sequence. |
| Outcome: | The proposed method outperforms baseline models on the inside Airbnb and StackSample datasets. |
Can Large Language Models Understand Context? (2024.findings-eacl)
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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng
| Challenge: | Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features. |
| Approach: | They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation. |
| Outcome: | The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models. |
Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs. |
| Approach: | They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts. |
| Outcome: | The proposed framework outperforms baseline methods on personalized review generation. |
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)
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| Challenge: | Existing attempts to integrate singleton mention detection into end-to-end coreference resolution for English have been hampered by the lack of singletont mention spans in the OntoNotes benchmark. |
| Approach: | They propose a two-step neural mention and coreference resolution system that integrates singleton mentions with OntoNotes syntax trees to achieve a near approximation of the Ontonotes dataset with all singletont mentions. |
| Outcome: | The proposed system achieves 94% recall on a sample of gold singletons. |
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains (2024.emnlp-main)
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Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu, Shabnam Behzad, Lauren Levine, Jessica Lin, Devika Tiwari, Amir Zeldes
| Challenge: | Existing shallow discourse parsing systems focus on the Wall Street Journal corpus, but the data is limited to the news domain and is 35 years old. |
| Approach: | They propose to use the Wall Street Journal corpus as a benchmark for PDTB-style shallow discourse parsing. |
| Outcome: | The proposed dataset is compatible with PDTB, but suffers from degradation out-of-domain. |
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)
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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. |
| Approach: | They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). |
| Outcome: | The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations. |
OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres (2021.acl-short)
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| Challenge: | Existing methods for coreference resolution are unable to evaluate generalizability to open domain data. |
| Approach: | They propose to make an OntoNotes-like coreference dataset publicly available and convert it into an English corpus. |
| Outcome: | The proposed dataset is the largest human-annotated coreference corpus following the OntoNotes guidelines and the first to be evaluated for consistency with the OnToNote's scheme. |
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction (2026.findings-acl)
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| Challenge: | Existing methods for text generation require multiple independent sequences to be decoded in parallel. |
| Approach: | They propose an algorithm that accelerates offline decoding by leveraging shared memory and computation across batches. |
| Outcome: | Experiments show that attribute-value pairs are conditionally independent, enabling decoding in parallel up to 96 tokens per prompt. |
A Corpus of Adpositional Supersenses for Mandarin Chinese (2020.lrec-1)
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| Challenge: | Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. |
| Approach: | They propose to annotate Chinese adpositions in a corpus with all aforementioned supersenses . they adapt a framework that defined a set of supersens according to ostensibly language-independent criteria . |
| Outcome: | The proposed corpus is the first to be broadly annotated with adposition semantics in Chinese . it shows that the supersense categories are well-suited to Chinese adepositions despite syntactic differences from English . |
AMALGUM – A Free, Balanced, Multilayer English Web Corpus (2020.lrec-1)
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| Challenge: | a corpus of 4M tokens is available online with a large number of high-quality annotation layers. |
| Approach: | They propose to use a genre-balanced English web corpus with multiple annotation layers . they harness knowledge from multiple annotation layer to achieve a "better than NLP" benchmark . |
| Outcome: | The proposed corpus is genre-balanced and features high-quality automatic annotation layers. |