Papers by Weidong Guo
ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis (2023.acl-long)
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| Challenge: | Multimodal sentiment analysis aims to predict the sentiment of video content. |
| Approach: | They propose a framework that performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. |
| Outcome: | The proposed framework outperforms baseline methods on CH-SIMS, MOSI and MOSEI datasets on a range of metrics. |
Instruction Fusion: Advancing Prompt Evolution through Hybridization (2024.acl-long)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks, namely HumanEval, HumanEva+, MBPP, mbap+ and MultiPL-E. |
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution (2025.coling-main)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks. |
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis (2023.findings-acl)
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Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu
| Challenge: | Existing methods for multimodal sentiment analysis focus on general knowledge, which is inadequate to identify specific sentiments across modalities. |
| Approach: | They propose a method where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. |
| Outcome: | The proposed method outperforms all prior methods on three popular benchmarks on multimodal sentiment analysis metrics. |
MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)
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| Challenge: | Existing methods for text ranking have improved performance, but there are still challenges. |
| Approach: | They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. |
| Outcome: | The proposed method outperforms all prior methods on datasets with extensive results. |
Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)
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| Challenge: | Existing benchmarks and evaluation protocols focus on surface-level factual recall. |
| Approach: | They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect. |
| Outcome: | The proposed framework reveals failures not captured by existing benchmarks. |
Contrastive Learning enhanced Author-Style Headline Generation (2022.emnlp-main)
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| Challenge: | Current work only uses the article itself in the headline generation, but have not taken the writing style of headlines into account. |
| Approach: | They propose a model which takes historical headlines into account to integrate the stylistic features of the author into the model and integrate them into the decoder. |
| Outcome: | The proposed model can integrate the stylistic features of the author into the model and generate a headline that is appropriate for the article and consistent with the author’s style. |
Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation (2023.emnlp-main)
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| Challenge: | Mis- and disinformation online are a major source of harms of different kinds . out-of-context information is where different pieces of information are falsely associated . past studies have attempted to defend against OOC mis- and deinformation through external evidence, but they disregard the role of different pieces with different stances. |
| Approach: | They propose a stance extraction network that can extract stances of different pieces of evidence in a single framework. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a public large-scale dataset with a performance gain of 3.2% in accuracy. |
KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)
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| Challenge: | Existing evaluation metrics suggest that Multimodal large language models have acquired fine-grained visual grounding capabilities. |
| Approach: | They propose a benchmark to assess Referring Expression Comprehension (REC) that uses intra-image visual cues to localize target objects and a controllable evaluation mechanism to test sensitivity to fine-grained factual changes. |
| Outcome: | The proposed benchmarks show that multimodal large language models have a high level of performance on the RefCOCO family of benchmarks. |
Exploiting Hierarchically Structured Categories in Fine-grained Chinese Named Entity Recognition (2023.findings-acl)
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| Challenge: | Named Entity Recognition (CNER) is a widely used technology in various applications. |
| Approach: | They propose a method that uses a custom-designed relevance scoring function to learn the potential relevance between different flattened hierarchical labels. |
| Outcome: | The proposed method outperforms the state-of-the-art on the FiNE dataset. |
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization (2021.findings-acl)
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| Challenge: | Pre-trained language models are trained based on single-grained tokenization, making it hard to learn the precise meaning of coarse-grain words and phrases. |
| Approach: | They propose a language model pretraining method that incorporates multi-grained information of input text into pre-trained language models. |
| Outcome: | The proposed method improves performance on CLUE and SuperGLUE in Chinese and English with little extra inference cost. |
TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training (2026.acl-long)
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Yifan Wang, null Binbinliu, Fengze Liu, Yuanfan Guo, Jiyao Deng, Xuecheng Wu, Weidong Zhou, Xiaohuan Zhou, Taifeng Wang
| Challenge: | Static data mixing strategies in large language models are often suboptimal as they fail to adapt to the model’s evolving learning states. |
| Approach: | They propose a semi-dynamic data mixing framework that uses a key observation of influence ranking invariance to reduce computational overhead by 80% . |
| Outcome: | The proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, effectively mitigating data under-digestion. |
MagicBench: Diagnosing Visual Agency Loss and Semantic Dependency in Multimodal LLMs (2026.acl-long)
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| Challenge: | MLLMs assume linguistic context invariably enhances visual understanding . a diagnostic benchmark is used to evaluate ML models under hierarchical linguistic interference . |
| Approach: | They propose a diagnostic benchmark to evaluate MLLMs under hierarchical linguistic interference. |
| Outcome: | The proposed benchmark compared 402 videos with a physical constraint set to evaluate MLLMs under hierarchical linguistic interference. |