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pro vyhledávání: '"Morishita, Terufumi"'
Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (ALT)}$, which aims to enhance LLMs' reasoning capabilities by program-g
Externí odkaz:
http://arxiv.org/abs/2411.12498
Autor:
Yamaguchi, Atsuki, Morishita, Terufumi
We present appjsonify, a Python-based PDF-to-JSON conversion toolkit for academic papers. It parses a PDF file using several visual-based document layout analysis models and rule-based text processing approaches. appjsonify is a flexible tool that al
Externí odkaz:
http://arxiv.org/abs/2310.01206
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:25254-25274, 2023
We study a synthetic corpus based approach for language models (LMs) to acquire logical deductive reasoning ability. The previous studies generated deduction examples using specific sets of deduction rules. However, these rules were limited or otherw
Externí odkaz:
http://arxiv.org/abs/2308.07336
Publikováno v:
In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, October 21-25, 2023, Birmingham, United Kingdom. ACM, New York, NY, USA, 5 pages
Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatically creating one remains a challenge even with the recent advancement
Externí odkaz:
http://arxiv.org/abs/2308.03099
This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study. The tokenizer for such la
Externí odkaz:
http://arxiv.org/abs/2306.09572
Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts. Although simpler and computationally efficient pretraining objectives
Externí odkaz:
http://arxiv.org/abs/2305.10992
Publikováno v:
Proceedings of the 16th International Natural Language Generation Conference, 2023, pages 407 to 413
One of the challenges in text generation is to control text generation as intended by the user. Previous studies proposed specifying the keywords that should be included in the generated text. However, this approach is insufficient to generate text t
Externí odkaz:
http://arxiv.org/abs/2304.09516
We propose a fundamental theory on ensemble learning that answers the central question: what factors make an ensemble system good or bad? Previous studies used a variant of Fano's inequality of information theory and derived a lower bound of the clas
Externí odkaz:
http://arxiv.org/abs/2205.12683
Publikováno v:
in Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for fiv
Externí odkaz:
http://arxiv.org/abs/1910.01299
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