Papers by Yong Cheng
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot (2025.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs). |
| Approach: | They introduce CoT to exemplars of ICL to enhance the reasoning capability . however, it remains unclear whether CoT exemplar is still beneficial for recent, stronger models in such tasks. |
| Outcome: | The enhanced exemplars fail to improve the model’s reasoning performance, despite being constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1. |
Depression Detection on Social Media with Large Language Models (2025.emnlp-industry)
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| Challenge: | Existing methods for analyzing social media data lack a systematic integration of medical knowledge, causing a critical treatment gap. |
| Approach: | They propose a framework that leverages Large Language Models to integrate medical knowledge into social media data. |
| Outcome: | The proposed framework can be used to distinguish depression from transient mood changes. |
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)
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| Challenge: | Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points . |
| Approach: | They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points. |
| Outcome: | The proposed model surpasses existing models on ArgKP and QAM datasets. |
Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods, such as a n-terminal coding, do not provide accurate data for large language models. |
| Approach: | They propose a lightweight framework that leverages attention distributions and uncertainty signals in a single-pass decoding. |
| Outcome: | Experiments on open-book QA datasets show that DAGCD improves faithfulness and robustness while preserving computational efficiency. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)
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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. |
| Approach: | They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments. |
| Outcome: | The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods. |
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach (P19-1)
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| Challenge: | Existing methods for reducing word omission errors in neural machine translation are prone to omit essential words on the source side. |
| Approach: | They propose a contrastive learning approach to reduce word omission errors in NMT by omitting words. |
| Outcome: | The proposed approach achieves better translation performance than baseline methods on Chinese-to-English, German-to English, and Russian-toEnglish translation tasks. |
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game (2024.findings-acl)
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| Challenge: | Existing methods for training large language models require additional annotations to adjust to shifted distributions. |
| Approach: | They propose an algorithm that allows LLMs and reward models to update alternatively via a min-max game to improve their alignment. |
| Outcome: | The proposed framework improves existing alignment baselines in terms of LLM helpfulness and harmlessness. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to train multilingual models to learn the inductive bias of a shared vocabulary and set of parameters across languages. |
| Approach: | They propose to use a multilingual crossover encoder-decoder to fuse language pairs at an instance level to encourage sharing of input and output spaces. |
| Outcome: | The proposed approach improves quality on English-to-Many, Many-to English and zero-shot translation tasks from +0.5 BLEU up to +5.5 BLUE points. |
On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods. |
| Approach: | They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences. |
| Outcome: | The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance. |
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2024.naacl-long)
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Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao
| Challenge: | Large Language Models (LLMs) have revolutionized the landscape of artificial intelligence. |
| Approach: | They propose a self-guided method to identify and select cherry samples from open-source datasets, minimizing manual curation and potential cost for instruction tuning an LLM. |
| Outcome: | The proposed method enables LLMs to identify discrepancies between expected responses and intrinsic generation capability, and a marked uptick in model training efficiency. |
PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are too large to be fine-tuned with budget constraints and some are only accessible via APIs. |
| Approach: | They propose a pluggable Reward-Driven Contextual Adapter that integrates large language models as generators and trains them to refine the retrieved information. |
| Outcome: | The proposed method improves ReQA performance on three datasets by up to 20% compared to existing methods. |
HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation (2025.emnlp-main)
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| Challenge: | Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data. |
| Approach: | They propose a Hierarchical Sampling framework for self-taught reasoners that allocates a fixed sampling budget to problem boundary-level problems and then reallocates the remaining budget toward high-utility problems during a re-sampling phase. |
| Outcome: | The proposed framework outperforms baseline models without additional sampling budgets across multiple reasoning benchmarks and backbone LLMs. |
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems. |
| Approach: | They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space . |
| Outcome: | The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks. |
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces. |
| Approach: | They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters. |
| Outcome: | The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios. |
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024.acl-long)
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| Challenge: | Earlier studies of instruction tuning on Large Language Models focus on creating large, varied, and high-quality datasets with responses curated by human experts. |
| Approach: | They propose to use a smaller and weaker model to fine tune a larger and stronger model . they find it can largely speed up the data filtering and improve performance . |
| Outcome: | The proposed model can filter instruction data faster and better on benchmarks. |
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots. |
| Approach: | They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations. |
| Outcome: | The proposed approach achieves 52.68% and 58.55% joint accuracy on multiWOZ 2.0 and MultiWOZ 2.1 datasets and significantly improves performance (+1.24% and +5.98%) |
GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression (2025.emnlp-main)
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| Challenge: | Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost. |
| Approach: | They propose a framework that removes redundant layers to reduce inference cost by preserving sensitivity-aware singular values. |
| Outcome: | The proposed framework outperforms existing methods in 90% of the original model under a 20% compression ratio. |
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)
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| Challenge: | Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction . |
| Approach: | They propose a framework that solves a query step-by-step and performs stepwise self-correction. |
| Outcome: | The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points. |
Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)
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| Challenge: | Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. |
| Approach: | They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios. |
| Outcome: | The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities. |
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers (2024.acl-long)
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| Challenge: | Existing transformer-based models struggle with long-sequence processing due to computational costs . a framework to enhance long-content processing of transformers is proposed . |
| Approach: | They propose a framework to enhance long-sequence processing of transformers by three steps . they demonstrate that the framework significantly outperforms prior long-quence processors . |
| Outcome: | The proposed framework outperforms baseline models on long-sequence summarization and reading comprehension tasks. |
Towards Robust Neural Machine Translation (P18-1)
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| Challenge: | Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation models. |
| Approach: | They propose adversarial stability training to make encoder and decoder robust to perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. |
| Outcome: | The proposed approach improves translation quality and robustness over strong models on Chinese-English, English-German and English-French translation tasks. |
Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)
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| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
| Approach: | They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input. |
| Outcome: | The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data. |
An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |