Search Results for author: Hailin Chen

Found 8 papers, 4 papers with code

Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation

1 code implementation28 Oct 2023 Hailin Chen, Amrita Saha, Steven Hoi, Shafiq Joty

With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs.

Code Generation

CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules

1 code implementation13 Oct 2023 Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, Shafiq Joty

We find that by naturally encouraging the LLM to reuse the previously developed and verified sub-modules, CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.

Ranked #2 on Code Generation on CodeContests (Test Set pass@1 metric)

Code Generation

PromptSum: Parameter-Efficient Controllable Abstractive Summarization

no code implementations6 Aug 2023 Mathieu Ravaut, Hailin Chen, Ruochen Zhao, Chengwei Qin, Shafiq Joty, Nancy Chen

Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios.

Abstractive Text Summarization Language Modelling

Retrieving Multimodal Information for Augmented Generation: A Survey

no code implementations20 Mar 2023 Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty

As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world.

Retrieval

Learning Label Modular Prompts for Text Classification in the Wild

1 code implementation30 Nov 2022 Hailin Chen, Amrita Saha, Shafiq Joty, Steven C. H. Hoi

Machine learning models usually assume i. i. d data during training and testing, but data and tasks in real world often change over time.

text-classification Text Classification

Incorporating Consistency Verification into Neural Data-to-Document Generation

no code implementations15 Aug 2018 Feng Nie, Hailin Chen, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin, Rong pan

Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts.

reinforcement-learning Reinforcement Learning (RL) +1

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