Papers by Hongjin Su
EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)
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| Challenge: | Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion. |
| Approach: | They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases. |
| Outcome: | The proposed pipeline achieves two to four times of execution accuracy compared to other methods. |
One Embedder, Any Task: Instruction-Finetuned Text Embeddings (2023.findings-acl)
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Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | a new method for embedding text is developed for tasks that require specialized encoders . INSTRUCTOR is a single embedder that can generate text embeddables tailored to different tasks and domains based on instruction finetuning . |
| Approach: | They introduce a new method for computing text embeddings given task instructions . they first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture . |
| Outcome: | The proposed method improves on 70 embedding evaluation tasks with fewer parameters than the previous best model. |
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation (2021.acl-long)
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| Challenge: | Existing methods to train pre-trained models require domain-specific data and computational resources. |
| Approach: | They propose a domain-aware N-gram Adaptor to incorporate unseen and domain-specific words into a generic pretrained model. |
| Outcome: | The proposed model can improve on eight low-resource tasks using limited data with lower computational costs. |