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Atsuki Yamaguchi

7 accepted papers

2024

An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference

EMNLP 2024finding

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorate…

2024

CHICOT: A Developer-Assistance Toolkit for Code Search with High-Level Contextual Information

AAAI 2024technical

We propose a source code search system named CHICOT (Code search with HIgh level COnText) to assist developers in reusing existing code. While previous studies have examined code search on the basis of code-level, fine-grained specifications such as functionality, logic, or implementation, CHICOT ad…

Cited by 0SourcePDFScholar
2024

Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus

NeurIPS 2024poster

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-generated logical reasoning samples. We first estab…

2024

JFLD: A Japanese Benchmark for Deductive Reasoning Based on Formal Logic

COLING 2024main

Large language models (LLMs) have proficiently solved a broad range of tasks with their rich knowledge but often struggle with logical reasoning. To foster the research on logical reasoning, many benchmarks have been proposed so far. However, most of these benchmarks are limited to English, hinderin…

2023

How does the task complexity of masked pretraining objectives affect downstream performance?

ACL 2023findings

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, e.g., predicting the first character of a masked…

2023

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

ICML 2023poster

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 otherwise arbitrary. This can limit the generalizability…

2021

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

EMNLP 2021main

Masked language modeling (MLM), a self-supervised pretraining objective, is widely used in natural language processing for learning text representations. MLM trains a model to predict a random sample of input tokens that have been replaced by a [MASK] placeholder in a multi-class setting over the en…