Papers by Yun Zhou
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| Challenge: | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). |
| Approach: | They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively. |
| Outcome: | The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score. |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)
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| Challenge: | Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents. |
| Approach: | They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem. |
| Outcome: | The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders. |
A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)
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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong
| Challenge: | Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices. |
| Approach: | They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features. |
| Outcome: | The proposed framework aims to improve the performance of large language models on various tasks. |
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)
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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh
| Challenge: | Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts. |
| Approach: | They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy. |
| Outcome: | Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model. |
Efficient Training for Cross-lingual Speech Language Models (2026.findings-acl)
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| Challenge: | Currently, large language models (LLMs) focus on the text modality, making speech modeling difficult. |
| Approach: | They propose a cross-lingual speech language model that trains on discrete speech tokens to achieve cross-modal and cross-linguistic alignment through continual pre-training. |
| Outcome: | The proposed method achieves cross-modal and cross-lingual alignment through continual pre-training. |
SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)
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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao
| Challenge: | Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions. |
| Approach: | They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels . |
| Outcome: | The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing. |
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)
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| Challenge: | Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. |
| Approach: | They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data. |
| Outcome: | The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences. |
CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction (2026.findings-acl)
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Jun Gao, Yun Peng, Qian Qiao, Changhai Zhou, Yuhua Zhou, Shiyang Zhang, Shichao Weng, Zhenchang Xing, Xiaoxue Ren
| Challenge: | Existing code reasoning benchmarks evaluate final output correctness under a single implementation. |
| Approach: | They propose a Code Reasoning benchmark that evaluates code reasoning through implementation invariance and process transparency. |
| Outcome: | The proposed benchmarks lack implementation invariance and process transparency . they observe superficial execution where models arrive at correct outputs without reasoning . |
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)
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| Challenge: | Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration. |
| Approach: | They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification. |
| Outcome: | The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions. |
Learning to Ideate for Machine Learning Engineering Agents (2026.eacl-short)
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Yunxiang Zhang, Kang Zhou, Zhichao Xu, Kiran Ramnath, Yun Zhou, Sangmin Woo, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. |
| Approach: | They propose a framework that separates ideation from implementation that allows an implementation agent to request strategic help from a dedicated Ideator. |
| Outcome: | The proposed framework outperforms implementation-only agent baselines on MLE-Bench and can be trained with reinforcement learning to generate more effective ideas. |
Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models (2025.naacl-short)
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| Challenge: | Existing methods to mitigate object hallucination are impractical for proprietary LVLMs. |
| Approach: | They propose a framework to identify optimal visual prompts that enhance LVLM responses without access to model internals. |
| Outcome: | The proposed approach is model-agnostic and can be used on open-source and proprietary LVLMs. |
BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generality, often solving tasks with a single carefully engineered prompt. |
| Approach: | They propose to cast automatic workflow generation as Bayesian inference over a posterior distribution on workflows and instantiate BayesFlow as Bayer-based workflow generation framework. |
| Outcome: | The proposed framework improves accuracy by 9 percentage points over baselines and 65 percentage points on pool-wide benchmarks. |
CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing (2024.emnlp-main)
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| Challenge: | Existing methods to improve code generation from natural language descriptions are difficult due to complex structure, subtle bugs, and lack of supplementary contents. |
| Approach: | They propose a framework that enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. |
| Outcome: | The proposed framework improves the quality of complex code generation on the DS-1000 and ClassEval datasets. |
InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)
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| Challenge: | Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks . |
| Approach: | They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking. |
| Outcome: | Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data. |
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2020.acl-main)
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| Challenge: | Existing knowledge graph embeddings have improved the knowledge graph link prediction task, but complex relations such as N-to-1, 1-to-N and N- to-N remain challenging to predict. |
| Approach: | They propose to extend RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations. |
| Outcome: | The proposed method improves on N-to-1, 1-to-N and N- to-N cases while maintaining the capability for modeling symmetric/anti-symmetric, inverse and compositional relations. |
Deriving Character Logic from Storyline as Codified Decision Trees (2026.acl-long)
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| Challenge: | Existing behavioral profiles are unstructured, weakly validated, and unusable . existing models are weakly valid, leading to brittle agent behavior . Using codified decision trees, we show that CDT outperforms previous methods . |
| Approach: | They propose a data-driven framework that induces an executable decision structure from narrative data. |
| Outcome: | The proposed framework outperforms human-written profiles and prior profiles on multiple benchmarks. |
FreezeEmpath: Efficient Training for Empathetic Spoken Chatbots with Frozen LLMs (2026.findings-acl)
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| Challenge: | Recent research has made significant progress in developing empathetic spoken chatbots based on large language models (LLMs). |
| Approach: | They propose an end-to-end empathetic spoken chatbot trained efficiently that generates emotionally expressive speech and outperforms other emmpathetic models in emphatic dialogue, SER, and SpokenQA tasks. |
| Outcome: | The proposed model outperforms other empathetic models on e-dialog, SER, and SpokenQA tasks and achieves strong results on several speech tasks. |
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing. |
| Approach: | They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances. |
| Outcome: | Empirical results show that the proposed model improves against baselines and can be scaled to a large extent. |
CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels (2026.acl-long)
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Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong
| Challenge: | Existing approaches to support diverse attention variants trade performance for flexibility . expert-written kernels achieve high efficiency but are difficult to adapt . |
| Approach: | They propose a framework that adapts expert-written attention kernels to GPUs . they use a structured lift–transfer–lower workflow to make execution explicit . |
| Outcome: | The proposed framework outperforms existing frameworks and compilers on diverse variants and GPU platforms. |