Papers by Zeang Sheng
Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)
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| Challenge: | Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination . |
| Approach: | They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality . |
| Outcome: | The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs. |
Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval? (2026.findings-acl)
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| Challenge: | Rapid advances in multimodal large language models have revolutionized cross-modality understanding. |
| Approach: | They propose a method that uses whitening transformations to adjust MLLM representation spaces . they propose ML models that are dominated by textual semantics and visual semantics . |
| Outcome: | The proposed approach improves zero-shot multimodal retrieval performance without fine-tuning efforts. |