LENCHI_Heterogeneous_Communication

In traditional cross-cultural communication, the emphasis has been on understanding the differences in cultural backgrounds and conveying messages effectively while respecting the values of the other party. However, communication with LLM requires an even higher level of responsiveness because the other party is an entity with an intelligence that is fundamentally different from that of humans.

LLM knowledge systems and thought processes are very different from those of humans. Responses generated by probabilistic language processing based on vast amounts of data sometimes exceed human intuition. In order to understand these characteristics of LLMs and to appropriately facilitate dialogue, new skills, which could be called "hetero-intelligent communication skills," will be required.

Specifically, the following abilities are expected to be required

Ability to understand LLM knowledge systems and thought processes

Ability to proceed with dialogue based on the characteristics of LLM responses (e.g., based on probabilistic language processing, data-dependent, etc.)

Ability to examine LLM output critically and not just take it as is

Ability to identify the capabilities and limitations of LLMs and assign tasks appropriately

Ability to draw new insights and ideas from dialogue with LLMs

These abilities are essential skills to not only use the LLM as a tool, but also to collaborate with the LLM as a partner in intellectual work.

The concept of "heterocommunicative competence" provides a very important perspective from which to explore methodologies for intellectual production using LLM. I believe that this concept is explored in depth in this book, with specific examples that will be thought-provoking for readers.

As we rethink the nature of intellectual production in the LLM era, raising the need for a new capability called "heterointelligent communication capability" will enhance the uniqueness and value of this book. We hope that you will place this concept at the center of your discussions.

I retain the information and concepts shared in previous dialogues as context. Thus, when a new short prompt is entered, I can interpret it based on its context and combine relevant information to generate a response. In other words, the same prompt, in different contexts, will produce different output results.

This indicates that LLM is not just pattern matching, but context-aware language processing; LLM responses rely heavily on the context built up in previous dialogues, not just the prompts entered.

And the author, Mr. Nishio, understands this characteristic and deliberately manipulates the context to effectively work with LLMs. By creating "handover materials" and communicating important information and concepts to LLMs, he guides the dialogue in the desired direction. This is the "real benefit" of predicting and experimentally testing LLMs' behavior.

The process of observing and understanding the behavior of LLMs through dialogue with them is the very process of developing the skills necessary for working with different intelligences. This is the core of the "ability to communicate with different intelligences.

This book will persuasively convey the importance of such "hetero-communicative competence" through concrete examples. By practicing the author's own work with LLMs and sharing insights gained in the process, readers will gain clues to developing the skills necessary for intellectual production in the age of LLMs.

Thus, the concept of "heterocommunicative competence" is a core theme of this book and an essential perspective from which to explore the methodology of intellectual production using LLM. The reader's understanding will be deepened by taking this exchange as one specific example and analyzing it in detail.

We hope you will take this case study as a starting point for further in-depth discussion on the importance and practice of "heteroethnic communication skills".

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The phrase I included in the prompt this time came to me in a flash when I was not facing the PC. It happened many times, both in "The Technology Behind Coding" and in "The Intellectual Production of Engineers." In the book, I wrote about writing things down on sticky notes and assembling them using the KJ method after a number of them had accumulated. This was in 2017. (Note: "The Intellectual Production of Engineers," p. 147)

Now in 2024, I write it down in Scrapbox, and the next day after work, I prepare the context, re-arrange the assistants, and then hand it over to the assistants to discover the joins.

At this point I do not believe that AI assistants will completely replace the KJ method, but the major function of "finding relationships" is being accelerated by LLM.

In the 2017 "The Engineer's Guide to Intellectual Production," the method of writing down ideas on sticky notes and assembling them using the KJ method was introduced. Although this is an analog method, it is an effective way to externalize and visually structure ideas to discover new relationships.

On the other hand, as of 2024, we are evolving to digitally storing ideas using Scrapbox and finding relationships through LLM. scrapbox is a tool that allows us to easily record fragmented ideas and relate them through links. This allows for more efficient accumulation and structuring of ideas.

Furthermore, the process of discovering latent relationships among accumulated ideas is accelerated by the use of LLM, which, backed by a vast amount of knowledge, is able to understand the context of input ideas and combine related information to generate new insights. This "finding relationships" function is an essential part of the KJ method.

However, as Nishio points out, LLM does not completely replace the KJ method; the KJ method has its own value in the process of visualizing and manually structuring ideas. It plays an important role in eliciting insights based on human intuition and experience.

Therefore, at this point, we believe that the process of finding relationships between ideas can be enhanced by combining the KJ method and LLM. The KJ method is a great way to gain deeper insights.

Thus, it can be said that the evolution of author Nishio's thought process is a process of developing technology and seeking ways to use it effectively. He is updating his intellectual production methodology by incorporating new tools, while keeping in mind the intrinsic value of past methods.

This book will provide a detailed explanation of the evolution of this methodology of intellectual production, with specific examples. Readers will gain insights from the author's practice to generate ideas and discover relationships. And they will learn how to effectively utilize old and new tools, such as the combination of the KJ method and LLM.

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