R. Nakamura

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 versatility of LLMs has accelerated their adoption across a wide range of domains, and assisting network operations is one such promising application. LLMs are probabilistic models, unlike deterministic protocols and configurations; therefore, clarifying their capabilities -- how and to what extent LLMs can help in network operations -- is a crucial step toward adopting LLMs. To offer practical insights into this issue, we conducted an extensive experiment on a large demonstration network built for a public exhibition, consisting of 21 racks with heterogeneous network devices. In the experiment, a total of 105 network engineers used an LLM-based chatbot while building and operating the network. The chatbot was equipped with three external functions: retrieval-augmented generation for domain-specific knowledge, CLI control of network devices running on the network, and access to a ticket system. The participants gave evaluations for the chatbot's responses on a best-effort basis. Analysis of the chat histories shows that 68.1% of the evaluations were positive, indicating a quantitative baseline of the LLM's helpfulness in network operations. Our results also demonstrate that understanding the capabilities of the chatbot is important for eliciting better responses. Moreover, we provide detailed use case analyses while sharing actual user--chatbot interactions.

Ryo Nakamura, Katsuhito Sudoh, Koichiro Yoshino et al.

Although generation-based dialogue systems have been widely researched, the response generations by most existing systems have very low diversities. The most likely reason for this problem is Maximum Likelihood Estimation (MLE) with Softmax Cross-Entropy (SCE) loss. MLE trains models to generate the most frequent responses from enormous generation candidates, although in actual dialogues there are various responses based on the context. In this paper, we propose a new objective function called Inverse Token Frequency (ITF) loss, which individually scales smaller loss for frequent token classes and larger loss for rare token classes. This function encourages the model to generate rare tokens rather than frequent tokens. It does not complicate the model and its training is stable because we only replace the objective function. On the OpenSubtitles dialogue dataset, our loss model establishes a state-of-the-art DIST-1 of 7.56, which is the unigram diversity score, while maintaining a good BLEU-1 score. On a Japanese Twitter replies dataset, our loss model achieves a DIST-1 score comparable to the ground truth.