Papers by Chenyu You
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to generate informative titles for products with limited labels are inadequate for novel products. |
| Approach: | They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products. |
| Outcome: | The proposed approach achieves state-of-the-art results on novel product categories with limited labels. |
UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing information retrieval models assume a homogeneous structure for knowledge sources and user queries, limiting their applicability in real-world settings. |
| Approach: | They propose a unified instruction-aware heterogeneous knowledge retriever that builds a heterogenous retrieval space for heterogenized knowledge and follows diverse user instructions to retrieve knowledge in specified types. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on CompMix-IR . it achieves 6.36% relative improvements and 54.23% relative improvements . |
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. |
| Approach: | They propose a self-supervised training stage and a contrastive representation learning stage for spoken question answering with auxiliary tasks and augmentation strategies. |
| Outcome: | The proposed model achieves state-of-the-art results on three SQA benchmarks. |
Large Language Models Are Partially Primed in Pronoun Interpretation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies suggest large language models acquire rich linguistic representations, but little is known about whether they adapt to linguistic biases in a human-like way. |
| Approach: | They examine whether large language models display human-like referential biases using stimuli and procedures from real psycholinguistic experiments. |
| Outcome: | The proposed models display human-like referential biases when exposed to referential patterns in the local context. |
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for conversational question answering significantly degrade on datasets . a new task aims to enable systems to model complex dialogues flow given the speech documents . |
| Approach: | They propose a new Spoken Conversational Question Answering task to model human conversations . they propose DDNet, which ingests cross-modal information to achieve fine-grained representations of speech and language modalities. |
| Outcome: | The proposed method achieves superior performance in spoken conversational question answering. |
Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs (2025.acl-long)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompting has emerged as a practical workaround, but most CoT-based methods rely on a single generic prompt like “think step by step” with no task-specific adaptation. |
| Approach: | They propose a theoretical framework that explains why some prompts succeed while others fail by using a generic generic prompt like "think step by step" they show that prompts function as selectors, extracting specific task-relevant information from the model's full hidden state during CoT reasoning. |
| Outcome: | The proposed framework explains why some prompts succeed while others fail. |
DocAgent: An Agentic Framework for Multi-Modal Long-Context Document Understanding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to document understanding are limited due to limited context length or fail to fully leverage multi-modal information. |
| Approach: | They propose a multi-agent framework for long-context document understanding that imitates human reading practice. |
| Outcome: | The proposed framework surpasses human-level benchmarks on long-context document understanding while maintaining a short context length. |