Anthropomorphism in Large Language Models

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Source arXiv

Machine Learning

Computation and Language

Anthropomorphism in Large Language Models

Talking About Large Language Models

We live in an era of rapid progress in artificial intelligence, both within the field and in the public sphere.

The more adept large language models become at mimicking human language, the more vulnerable we become to anthropomorphism, to seeing the systems in which they are embedded as more human-like than they really are.

This trend is amplified by the natural tendency to use philosophically loaded terms, such as"knows","believes", and"thinks", when describing these systems.

To mitigate this trend, this paper advocates the practice of repeatedly stepping back to remind ourselves of how large language models, and the systems of which they form a part, actually work.

Authors

Murray Shanahan

Related Topics

Ai systems

Gpt - 3

Transformer architectures

Large language models

Llm-based conversational agent

Gpt - 2 @ cite1

Palm @ cite5

Introduction

We are building systems whose capabilities more and more resemble those of humans, despite the fact that those systems work in ways that are fundamentally different from the way humans work.

To ensure that we can make informed decisions about the trustworthiness and safety of the systems we deploy, it is advisable to keep to the fore the way those systems actually work, and thereby to avoid imputing to them capacities they lack, while making the best use of the remarkable capabilities they genuinely possess.

Result

The way we talk about large language models (llms) matters.

It matters not only when we write scientific papers, but also when we interact with policy makers or speak to the media.

The careless use of philosophically loaded words like “believes” and “thinks” is especially problematic, because such terms obfuscate mechanism and actively encourage anthropomorphism.

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Keep Up With Latest Trending Papers. Computer Science, AI and Machine Learning and more.Subscribe

Liked byVik

CC BY

Source arXiv

Machine Learning

Computation and Language

Anthropomorphism in Large Language Models

Talking About Large Language Models

We live in an era of rapid progress in artificial intelligence, both within the field and in the public sphere.

This trend is amplified by the natural tendency to use philosophically loaded terms, such as"knows","believes", and"thinks", when describing these systems.

To mitigate this trend, this paper advocates the practice of repeatedly stepping back to remind ourselves of how large language models, and the systems of which they form a part, actually work.

Authors

Murray Shanahan

Related Topics

Ai systems

Gpt - 3

Transformer architectures

Large language models

Llm-based conversational agent

Gpt - 2 @ cite1

Palm @ cite5

Introduction

We are building systems whose capabilities more and more resemble those of humans, despite the fact that those systems work in ways that are fundamentally different from the way humans work.

Result

The way we talk about large language models (llms) matters.

It matters not only when we write scientific papers, but also when we interact with policy makers or speak to the media.

The careless use of philosophically loaded words like “believes” and “thinks” is especially problematic, because such terms obfuscate mechanism and actively encourage anthropomorphism.

Read the Paper

Read Paper

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