Papers by Yucheng Lin
With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models and Vision Language Model (VLMs) have demonstrated aptitude as potential substitutes for human participants in psycholinguistic experiments. |
| Approach: | They examine whether large language models and vision language models implicitly understand sound-based phenomena via orthography and imagery alone. |
| Outcome: | The proposed models demonstrate sound symbolism and ability to "hear" using language and vision modules. |
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights (2024.findings-emnlp)
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| Challenge: | despite its importance, little work exists on modelling rigour in scientific writing . despite widespread use of term, scientific literature lacks definition of rigor . |
| Approach: | They propose a framework to automatically identify and define rigour criteria and assess their relevance in scientific writing. |
| Outcome: | The proposed framework can be tailored to the evaluation of scientific rigour for different areas. |
RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation (2026.findings-acl)
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| Challenge: | Scientific rigour tends to be sidelined in favour of bold statements, leading authors to overstate claims beyond what their results support. |
| Approach: | They propose a multimodal framework that retrieves supporting evidence from a paper and assigns each claim an overstatement score. |
| Outcome: | The proposed framework retrieves supporting evidence from ICLR and NeurIPS papers and assigns each claim an overstatement score. |
Compressing Context to Enhance Inference Efficiency of Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable power and impressive generalisation abilities across various tasks. |
| Approach: | They propose a method that prunes redundancies in the input context to make the input more compact. |
| Outcome: | The proposed method reduces memory and inference time while maintaining comparable performance compared to full context. |
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)
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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang
| Challenge: | Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos. |
| Approach: | They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies . |
| Outcome: | The proposed method is effective in a real-world video-search service. |
Metaphor Detection via Explicit Basic Meanings Modelling (2023.acl-short)
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| Challenge: | Existing methods for metaphor detection use the aggregated meaning of a word to approximate its basic meaning. |
| Approach: | They propose a method which models the basic meaning of a word based on literal annotations and compares this with the contextual meaning in a target sentence to identify metaphors. |
| Outcome: | The proposed method outperforms the state-of-the-art method significantly in the F1 score and even reaches the theoretical upper bound on the VUA18 benchmark. |
CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling (2022.coling-1)
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| Challenge: | Nominal metaphors are commonly used in human language and have been shown to be effective in persuading, expressing emotion, and stimulating interest. |
| Approach: | They propose a multitask framework which optimizes three tasks: NM identification, NM component identification, and NM generation. |
| Outcome: | The proposed framework outperforms baselines on consistency and creativity on the NM generation task in Chinese. |
Metaphor Detection with Effective Context Denoising (2023.eacl-main)
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| Challenge: | Existing models focus on semantically relevant information and provide a target-oriented parse tree structure for metaphor detection. |
| Approach: | They propose a new model which introduces a target-oriented parse tree structure for metaphor detection. |
| Outcome: | The proposed model achieves state-of-the-art on several main metaphor datasets and compares with other methods. |
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (2023.eacl-main)
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| Challenge: | Existing models for concept-level metaphor detection lack explicit knowledge of FrameNet . Metaphor detection is a pervasive linguistic device that is used in cognitive and communicative functions of language. |
| Approach: | They propose a BERT-based model that explicitly learns FrameNet Embeddings for metaphor detection. |
| Outcome: | The proposed model is more explainable and interpretable than existing models. |
Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark (2025.acl-long)
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Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng
| Challenge: | Existing RP-LLMs employ only a single role with numerous dialogues, but Crab enables dynamic configuration of desired roles, thereby enhancing related flexibility and adaptability. |
| Approach: | They propose a Configurable Role-Playing LLM with Assessing Benchmark that combines a Role dataset curation, persona-emodying Llm construction, and comprehensive benchmark creation for RP dialogue generation. |
| Outcome: | The proposed model outperforms existing LLMs in performing fine-grained evaluations of RP while keeping dialogue per role minimal. |
An Open-Source Data Contamination Report for Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing contamination analysis is conducted internally by large language model developers and lacks transparency and completeness. |
| Approach: | They present a data contamination report for 15 popular large language models . they propose an open-source pipeline to perform contamination analysis on customised data . |
| Outcome: | The proposed pipeline enables the community to perform contamination analysis on customised data and models. |