23 citations · 49 across the 15 of their papers we have counts for
11 papers · 1 filter
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models
Meiyun Wang, Masahiro Suzuki, Hiroki Sakaji +1
Large Language Models (LLMs) have demonstrated exceptional capabilities across various machine learning (ML) tasks. Given the high costs of creating annotated datasets for supervis…
SETN: Stock Embedding Enhanced with Textual and Network Information
Takehiro Takayanagi, Hiroki Sakaji, Kiyoshi Izumi
Stock embedding is a method for vector representation of stocks. There is a growing demand for vector representations of stock, i.e., stock embedding, in wealth management sectors,…
Discovery of Rare Causal Knowledge from Financial Statement Summaries
Hiroki Sakaji, Jason Bennett, Risa Murono +2
What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales would be suppressed as a result of…
Summarization of Investment Reports Using Pre-trained Model
Hiroki Sakaji, Ryotaro Kobayashi, Kiyoshi Izumi +2
In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In ad…
Economy Watchers Survey Provides Datasets and Tasks for Japanese Financial Domain
Masahiro Suzuki, Hiroki Sakaji
Natural language processing (NLP) tasks in English and general domains are widely available and are often used to evaluate pre-trained language models. In contrast, fewer tasks are…
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji
Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However,…
JaFIn: Japanese Financial Instruction Dataset
Kota Tanabe, Masahiro Suzuki, Hiroki Sakaji +1
We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more atten…
Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis
Takehiro Takayanagi, Masahiro Suzuki, Ryotaro Kobayashi +2
Causality is fundamental in human cognition and has drawn attention in diverse research fields. With growing volumes of textual data, discerning causalities within text data is cru…
JMedLoRA:Medical Domain Adaptation on Japanese Large Language Models using Instruction-tuning
Issey Sukeda, Masahiro Suzuki, Hiroki Sakaji +1
In the ongoing wave of impact driven by large language models (LLMs) like ChatGPT, the adaptation of LLMs to medical domain has emerged as a crucial research frontier. Since mainst…
From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models
Masahiro Suzuki, Masanori Hirano, Hiroki Sakaji
Instruction tuning is essential for large language models (LLMs) to become interactive. While many instruction tuning datasets exist in English, there is a noticeable lack in other…
llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology
Masanori Hirano, Masahiro Suzuki, Hiroki Sakaji
This study constructed a Japanese chat dataset for tuning large language models (LLMs), which consist of about 8.4 million records. Recently, LLMs have been developed and gaining p…