Papers by Yuanxin Liu

MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)

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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li

Challenge:	In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming.
Approach:	They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space.
Outcome:	The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis.

PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension (2025.acl-long)

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Kun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Multimodality

Challenge:	Existing benchmarks on punchline comprehension suffer from language shortcuts that allow models to rely on text, lack of question diversity, and narrow focus on a specific domain of multimodal content.
Approach:	They propose a multimodal punchline comprehension benchmark to assess models' ability to comprehend punchlines.
Outcome:	The proposed model surpasses in-context learning and chain-of-thought in punchline comprehension.

simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions (D18-1)

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Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Houfeng Wang, Xu Sun

Multimodality

Challenge:	Existing approaches to image captioning combine visual and semantic attention to generate a detailed and comprehensive caption.
Approach:	They propose a stepwise image-topic merging network that integrates visual and semantic attentions to generate a detailed caption.
Outcome:	The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performance.

TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)

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Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, Lu Hou

Visual Data in NLP

Challenge:	Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats .
Approach:	They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect .
Outcome:	The proposed benchmarks show that video large language models exhibit poor temporal perception ability.

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)

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Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Language Models

Challenge:	Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection.
Approach:	They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM.
Outcome:	The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead.

Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)

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Keqin Peng, Liang Ding, Yancheng Yuan, Xuebo Liu, Min Zhang, Yuanxin Ouyang, Dacheng Tao

Low-Resource NLP

Challenge:	Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL).
Approach:	They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods.
Outcome:	The proposed method improves language understanding and generation tasks with different model scales.

RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction (2025.emnlp-main)

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Yuchi Wang, Yishuo Cai, Shuhuai Ren, Sihan Yang, Linli Yao, Yuanxin Liu, Yuanxing Zhang, Pengfei Wan, Xu Sun

Multimodality

Challenge:	Existing recaptioning methods suffer from inaccuracies due to missing fine-grained details.
Approach:	They propose a framework that refines captions through visual reconstruction using a text-to-image model and a visual reconstruction framework.
Outcome:	The proposed framework outperforms baselines on CapsBench and CompreCap by 10%.

Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning (2022.findings-emnlp)

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Qingyi Si, Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Semantic Text Processing

Challenge:	Recent studies have shown that biased samples can be brittle for VQA models . however, the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data.
Approach:	They propose a contrastive learning approach that exploits biased samples for unbiased information that contributes to reasoning.
Outcome:	The proposed method achieves competitive performance on the OOD dataset while maintaining robustness on the ID dataset.

Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation (2021.acl-long)

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Yuanxin Liu, Fandong Meng, Zheng Lin, Weiping Wang, Jie Zhou

Language Models

Challenge:	Knowledge distillation (KD) has shown great success in BERT compression.
Approach:	They propose a knowledge distillation paradigm that extracts the teacher's hidden state knowledge and then compresses it into three dimensions.
Outcome:	The proposed paradigm gives rise to training speedup of 2.7x 3.4x for two kinds of student models and computing devices.

Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training (2022.naacl-main)

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Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Language Models

Challenge:	Recent studies show pre-trained language models contain matching subnetworks that have similar transfer learning performance as the original PLM.
Approach:	They propose to prune matching subnetworks using magnitude-based pruning . they propose to optimize the subnetwork structure towards the pre-training objectives .
Outcome:	The proposed method is more efficient in searching subnetworks and advantageous when fine-tuning within a range of data scarcity.

COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)

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Bowen Shen, Zheng Lin, Yuanxin Liu, Zhengxiao Liu, Lei Wang, Weiping Wang

Language Models

Challenge:	Existing statically compressed pre-trained language models lack spatial and temporal efficiency due to their large size and wide width.
Approach:	They propose a spatially and temporally efficient model which retains the major capacity of PLMs.
Outcome:	The proposed model retains the major capacity of pre-trained language models at high compression and acceleration rate with 1/8 parameters and 1/19 FLOPs of BERT.

Ranking and Sampling in Open-Domain Question Answering (D19-1)

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Yanfu Xu, Zheng Lin, Yuanxin Liu, Rui Liu, Weiping Wang, Dan Meng

Natural Language Interfaces

Challenge:	Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing.
Approach:	They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph.
Outcome:	Experiments on three datasets show that the proposed model advances the state of the art.

Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)

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Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, Dacheng Tao

Language Models

Challenge:	Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation.
Approach:	They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt.
Outcome:	The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks.

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022.findings-emnlp)

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Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Question Answering

Challenge:	Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution.
Approach:	They propose a dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Outcome:	The proposed dataset considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.

Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering (2023.emnlp-main)

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Qingyi Si, Yuanxin Liu, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang

Robustness in NLP

Challenge:	Existing studies on VQA models have found that they suffer from dataset biases and inefficient memory footprints.
Approach:	They investigate whether a VLP can be compressed and debiased simultaneously by searching sparse and robust subnetworks.
Outcome:	The proposed compression and debiasing pipelines outperform the debiased full VLPs on VQA tasks.