4 accepted papers
The learnware paradigm aims to construct a dock system that maintains a collection of learnwares, each consisting of a well-established model coupled with a specification, thereby enabling users to directly reuse existing models to solve their tasks without training models from scratch. As a core co
Despite their promising performance across various tasks, recent studies reveal that Large language models (LLMs) still exhibit significant deficiencies in handling several word-level and character-level tasks, e.g., word unscrambling and sentence editing, indicating urgent needs for substantial imp…
Recently, large language models (LLMs) have demonstrated breakthrough mathematical problem-solving capabilities in grade school math word problems (MWP). For example, on the MWP benchmark GSM8K, the accuracy of GPT-3.5-Turbo and MetaMath-70B reaches 80.80% and 82.30%, respectively. One question aris…
Learning from a limited amount of data namely Few-Shot Learning stands out as a challenging computer vision task. Several works exploit semantics and design complicated semantic fusion mechanisms to compensate for rare representative features within restricted data. However relying on naive semantic…