45 citations · 367 across the 61 of their papers we have counts for
4 papers · 1 filter
Controlling the Diversity-Quality Trade-Off of LLMs by Merging Models Before and After Alignment
Yasuaki Sumita, Tomoharu Iwata, Toshiyuki Tanaka
Alignment is known to change the output characteristics of large language models (LLMs). Base models tend to generate more diverse outputs, whereas aligned models tend to produce h…
Few-shot Learning for Topic Modeling
Tomoharu Iwata
Topic models have been successfully used for analyzing text documents. However, with existing topic models, many documents are required for training. In this paper, we propose a ne…
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora
Takashi Wada, Tomoharu Iwata, Yuji Matsumoto +2
We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word em…
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models
Takashi Wada, Tomoharu Iwata
We propose an unsupervised method to obtain cross-lingual embeddings without any parallel data or pre-trained word embeddings. The proposed model, which we call multilingual neural…