most citedEnhancing Financial Domain Adaptation of Language Models via Model Augmentation

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MA2026

Dynamic Haven Selection for Multi-Agent Pickup and Delivery in Constrained Warehouses

Taisei Hirayama, Kohei Yoshida, Hiroki Sakaji +1

Space-efficient warehouse layouts often contain single-agent-width aisles and dead-end workstations where robots have few places to wait without blocking others. In Multi-Agent Pic…

cs.MA2026

Fixed-Haven Reservation for Online Multi-Agent Pickup and Delivery in Dense Warehouses

Taisei Hirayama, Kohei Yoshida, Hiroki Sakaji +1

Dense warehouses often contain single-lane aisles, dead ends, and tree-like guidepaths that leave little room for idle agents to wait without blocking others. Existing Multi-Agent…

cs.CL2026

Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning

Yuna Haseyama, Tomoki Ito, Hiroki Sakaji +1

In high-stakes domains such as healthcare, the reliability of Large Language Models (LLMs) is critical, particularly when generating clinical insights from incident reports. This s…

cs.CL2024

Indexing Economic Fluctuation Narratives from Keiki Watchers Survey

Eriko Shigetsugu, Hiroki Sakaji, Itsuki Noda

In this paper, we design indices of economic fluctuation narratives derived from economic surveys. Companies, governments, and investors rely on key metrics like GDP and industrial…

cs.CL2024★ 3 cited

Enhancing Financial Domain Adaptation of Language Models via Model Augmentation

Kota Tanabe, Masanori Hirano, Kazuki Matoya +3

The domain adaptation of language models, including large language models (LLMs), has become increasingly important as the use of such models continues to expand. This study demons…