4 papers
LLM-jp-4 Technical Report
LLM-jp, :, Chaoran Liu +16
This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, and practical usability. LLM-…
How Helpful is LLM Assistance in Network Operations? A Case Study at a Large Demonstration Network
Ryo Nakamura, Koshi Eguchi
This paper reports on a real-world case study in which over 100 network engineers assessed how a Large Language Model (LLM) can assist in building and operating a network. The vers…
Steering at the Source: Style Modulation Heads for Robust Persona Control
Yoshihiro Izawa, Gouki Minegishi, Koshi Eguchi +2
Activation steering offers a computationally efficient mechanism for controlling Large Language Models (LLMs) without fine-tuning. While effectively controlling target traits (e.g.…
Extending the Context of Pretrained LLMs by Dropping Their Positional Embeddings
Yoav Gelberg, Koshi Eguchi, Takuya Akiba +1
So far, expensive finetuning beyond the pretraining sequence length has been a requirement for effectively extending the context of language models (LM). In this work, we break thi…