6 papers · 1 filter
Better Generalizing to Unseen Concepts: An Evaluation Framework and An LLM-Based Auto-Labeled Pipeline for Biomedical Concept Recognition
Shanshan Liu, Noriki Nishida, Fei Cheng +6
Generalization to unseen concepts is a central challenge due to the scarcity of human annotations in Mention-agnostic Biomedical Concept Recognition (MA-BCR). This work makes two k…
BiMax: Bidirectional MaxSim Score for Document-Level Alignment
Xiaotian Wang, Takehito Utsuro, Masaaki Nagata
Document alignment is necessary for the hierarchical mining (Bañón et al., 2020; Morishita et al., 2022), which aligns documents across source and target languages within the sam…
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation
Rikuto Tsuchida, Hibiki Yokoyama, Takehito Utsuro
The purpose of this paper is to examine whether large language models (LLMs) can understand what is good and evil with respect to judging good/evil reputation of celebrities. Speci…
Retrieval-Augmented Simulacra: Generative Agents for Up-to-date and Knowledge-Adaptive Simulations
Hikaru Shimadzu, Takehito Utsuro, Daisuke Kitayama
In the 2023 edition of the White Paper on Information and Communications, it is estimated that the population of social networking services in Japan will exceed 100 million by 2022…
Embedded Topic Models Enhanced by Wikification
Takashi Shibuya, Takehito Utsuro
Topic modeling analyzes a collection of documents to learn meaningful patterns of words. However, previous topic models consider only the spelling of words and do not take into con…
Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Data
Minato Kondo, Takehito Utsuro, Masaaki Nagata
In this paper, we propose a two-phase training approach where pre-trained large language models are continually pre-trained on parallel data and then supervised fine-tuned with a s…