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of 53
pro vyhledávání: '"Lee, Seonghyeon"'
We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a function. We design several ways to provide auxiliary functions to the mode
Externí odkaz:
http://arxiv.org/abs/2409.13928
Language models (LMs) have exhibited impressive abilities in generating codes from natural language requirements. In this work, we highlight the diversity of code generated by LMs as a critical criterion for evaluating their code generation capabilit
Externí odkaz:
http://arxiv.org/abs/2408.14504
Auxiliary function is a helpful component to improve language model's code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work, we comprehensively evaluate the ability to utilize auxiliary functio
Externí odkaz:
http://arxiv.org/abs/2403.10575
As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on a wide range of languages, a comprehensive evaluation of their profic
Externí odkaz:
http://arxiv.org/abs/2402.17377
Publikováno v:
The 37th AAAI conference on artificial intelligence (AAAI 2023)
Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that tra
Externí odkaz:
http://arxiv.org/abs/2302.13693
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic information, several recent studies have tried to expand (or complete) a t
Externí odkaz:
http://arxiv.org/abs/2211.01981
Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-the-art performance on the semantic textual similarity (STS) task. However, the absence of an interpretation method for the
Externí odkaz:
http://arxiv.org/abs/2202.13196
With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representati
Externí odkaz:
http://arxiv.org/abs/2111.11523
Autor:
Prayitno, Sara P., Natasha, Augustine, Lee, Seonghyeon, Kim, Choon-Mee, Lee, You Mi, Park, Kyungmin, Kim, Jongwoo, Kim, Seong-Gyu, Park, Jieun, Rajoriya, Shivani, Palacios, Gustavo, Oh, Yeonsu, Song, Jin-Won, Kim, Dong-Min, Kim, Won-Keun
Publikováno v:
In Clinical Microbiology and Infection June 2024 30(6):795-802
Recent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located. In this work, we propose a new approach
Externí odkaz:
http://arxiv.org/abs/2105.06750