Papers by Chengyue Wu
LanguageFlow: Advancing Diffusion Language Generation with Probabilistic Flows (2024.naacl-long)
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| Challenge: | Recent work has demonstrated success in controlling sentence attributes and structure based on diffusion language models. |
| Approach: | They propose a language-rectified flow method that reformulates standard probabilistic flow models to learn ordinary differential equations to transport between the source and target distributions. |
| Outcome: | The proposed method outperforms baselines on three fine-grained control tasks and multiple high-quality text editing tasks. |
COMBO: A Complete Benchmark for Open KG Canonicalization (2023.eacl-main)
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| Challenge: | Existing datasets for open KG canonicalization only provide gold entity-level canonization for noun phrases. |
| Approach: | They propose a complete benchmark for open KG canonicalization that provides gold ontology-level canonization for relation phrases and source sentences for extraction. |
| Outcome: | The proposed method improves relation canonicalization and ontology-level canonization of the noun phrase. |
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots (2025.findings-naacl)
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| Challenge: | Multi-modal Large Language Models have shown remarkable progress in visual contexts, yet their ability to convert visual figures into executable code remains underexplored. |
| Approach: | They propose to use a set of visual coding metrics to assess MLLMs' visual . pass rate, text-match ratio, and GPT-4V rating judgement to assess the quality of generated code and rendered images. |
| Outcome: | The proposed benchmark includes 132 high-quality matplotlib plots across six plot types, as well as 150 and 86 plots from Python’s and R’s plotly libraries respectively, totaling 368 plots. |
LLaMA Pro: Progressive LLaMA with Block Expansion (2024.acl-long)
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| Challenge: | Existing studies have demonstrated that pre-trained LLMs are limited in certain domains, such as programming, mathematics, biomedical, or finance. |
| Approach: | They propose a new post-pretraining method with an expansion of Transformer blocks to tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting. |
| Outcome: | The proposed model outperforms existing models in programming and math and its instruction-following counterpart LLaMA Pro-8.3B in general tasks, programming, and mathematics. |
Do PLMs Know and Understand Ontological Knowledge? (2023.acl-long)
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| Challenge: | Existing studies on pretrained language models focus mainly on factual knowledge, lacking a systematic probing of ontological knowledge. |
| Approach: | They investigate whether Pretrained Language Models store ontological knowledge and have a semantic un- derstanding of the knowledge rather than rote memorization of the surface form. |
| Outcome: | The proposed models can memorize certain ontological knowledge and perform logical reasoning with given knowledge according to ontological entailment rules. |
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field (2022.emnlp-main)
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| Challenge: | Entity typing assigns semantic types to entities mentioned in text. |
| Approach: | They propose to use an undirected graphical model to formulate the UFET problem by combining unary potentials with a pairwise conditional random field model. |
| Outcome: | The proposed model outperforms the existing model with little cost and is thousands of times faster than the existing neural network module. |