Papers by Yiming Hu
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles (2025.findings-acl)
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| Challenge: | Large language models are ideal for decision-making, but they can be difficult to process when they are verbose and include repetition, hedging, and vagueness. |
| Approach: | They propose a framework that constructs probabilistic factor profiles from complex scenarios and integrates them with analogical reasoning to guide LLMs in making decisions in new situations. |
| Outcome: | The proposed framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. |
Cross-Lingual Machine Reading Comprehension (D19-1)
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| Challenge: | Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data. |
| Approach: | They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches . |
| Outcome: | The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems. |
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations. |
| Approach: | They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions. |
| Outcome: | The proposed method is effective universally and further improves the performance of MRC tasks. |
CharBERT: Character-aware Pre-trained Language Model (2020.coling-main)
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| Challenge: | Pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations . but these methods split a word into subword units and make it incomplete and fragile . |
| Approach: | They propose a character-aware pre-trained language model to tackle OOV problems . they construct contextual word embedding for each token from sequential character representations . |
| Outcome: | The proposed model improves on the existing models on multiple NLP benchmarks. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Multimodal Invariant Sentiment Representation Learning (2025.findings-acl)
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| Challenge: | Existing methods for multimodal sensing ignore significant sentiment distribution imbalances and cross-modal sentiment conflicts, hindering performance improvement. |
| Approach: | They propose a method to learn stable multimodal invariant sentiment representations by incorporating distributional discrepancies and sentiment conflicts into the model training. |
| Outcome: | The proposed method improves MSA performance and achieves new state-of-the-art. |
Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models (2026.findings-acl)
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Xiaomeng Hu, Yixuan Tang, Haoze Li, Hao Chen, Qi Zhang, Zhanming Shen, Yiming Zhang, Haobo Wang, Junbo Zhao
| Challenge: | Prior work focused on data preprocessing, focusing on filtering and cleaning data . a study aimed to improve fine-grained scheduling of data order in epochs . |
| Approach: | They propose a fine-grained scheduling method of data order in epochs to fill this gap . they define data difficulty based on relevance between data and model . |
| Outcome: | The proposed method improves on pre-training and small-scale fine-tuning experiments 2.4% over baselines. |
Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . |
| Approach: | They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension . |
| Outcome: | The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) |
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks . |
| Approach: | They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates . |
| Outcome: | The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories . |
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)
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| Challenge: | Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference. |
| Approach: | They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code. |
| Outcome: | The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters. |
Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)
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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu
| Challenge: | Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps. |
| Approach: | They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling. |
| Outcome: | The proposed method outperforms open- and closed-source models on most metrics. |
A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them. |
| Approach: | They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets. |
| Outcome: | The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts. |
Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)
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| Challenge: | Existing studies focus on multi-hop question answering across multiple documents or paragraphs. |
| Approach: | They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results . |
| Outcome: | The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering . |
STRUX: An LLM for Decision-Making with Structured Explanations (2025.naacl-short)
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| Challenge: | a new LLM decision-making framework is designed to help users understand how and why decisions are made. |
| Approach: | They introduce a new LLM decision-making framework called STRUX that provides structured explanations for LLM decisions. |
| Outcome: | The proposed framework improves decision-making by providing structured explanations . it has been evaluated on the task of forecasting stock investment decisions based on earnings call transcripts - superior performance against strong baselines compared with previous frameworks based upon earnings call transcriptions demonstrating superior performance . |
Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020.findings-emnlp)
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| Challenge: | Existing pre-trained language models have shown tremendous improvements across various NLP tasks. |
| Approach: | They propose to revisit Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pretrained model series to the community. |
| Outcome: | The proposed model improves on RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). |
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)
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| Challenge: | Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively. |
| Approach: | They propose a framework that extracts feedback signals such as forgetting and uncertainty from rollout trajectories and utilizes them to guide LLM-based task synthesis. |
| Outcome: | Extensive experiments on AppWorld and BFCL show that the proposed framework improves over strong base models. |
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models (2025.findings-acl)
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Haoyang Li, Xuejia Chen, Zhanchao Xu, Darian Li, Nicole Hu, Fei Teng, Yiming Li, Luyu Qiu, Chen Jason Zhang, Li Qing, Lei Chen
| Challenge: | Existing benchmarks focus on linguistic competence or structured mathematical problem-solving, neglecting fundamental numerical reasoning required in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate numerical capabilities for large language models . they use a dataset to assess number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
| Outcome: | The proposed benchmark evaluates six fundamental numerical capabilities: number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data (2025.acl-long)
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| Challenge: | Existing studies focus on optimizing model structures to handle uncertain missingness, but models still face challenges when dealing with uncertain missing data. |
| Approach: | They propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion, which maps unimodal data to the latent space of Gaussian distributions to capture core features and structure. |
| Outcome: | The proposed method outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets. |
Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs? (2025.findings-emnlp)
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| Challenge: | Rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. |
| Approach: | They compare BERT-like models fine-tuning, LLM internal state utilization, and LLM zero-shot inference across six datasets. |
| Outcome: | The proposed method outperforms LLMs on six challenging datasets. |
Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese (2025.emnlp-main)
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Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu
| Challenge: | Translationese is a linguistic property that is often introduced in the translation process that is different from those of original texts. |
| Approach: | They propose to use synthesized translations and translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments. |
| Outcome: | The proposed measure can generalize to unseen genres, authors, and language pairs. |
DaNet: Dual-Aware Enhanced Alignment Network for Multimodal Aspect-Based Sentiment Analysis (2025.findings-acl)
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| Challenge: | Existing methods assume a direct alignment between images and aspects, matching the entire image with a corresponding aspect. Existing algorithms assume 'direct alignment' between images, introducing noise. |
| Approach: | They propose a Dual-Aware Enhanced Alignment Network (DaNet) that can enhance fine-grained multimodal aspect-image alignment and denoising. |
| Outcome: | The proposed system outperforms existing methods in three subtasks and is available on https://github.com/***/DaNet. |
Conversational Word Embedding for Retrieval-Based Dialog System (2020.acl-main)
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| Challenge: | Existing word embedding methods for retrieval-based dialog systems are based on co-occurrence statistics and train them based upon the same co-existence statistics. |
| Approach: | They propose a conversational word embedding method which uses the conversation pairs post, reply, and 'reply' they introduce a word alignment model from statistical machine translation and train it on word-level and sentence-level. |
| Outcome: | The proposed method improves the quality of the selected response on retrieval-based dialog systems. |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
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| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
| Outcome: | The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets. |