In a world where technology is advancing at an unprecedented pace, understanding the nuances of human language and behavior has never been more critical. The amount of data being produced globally every day is constantly increasing and it is difficult to give an exact number. In 2020, it was estimated the amount of data being produced daily was around 2.5 quintillion bytes.
As the amount of data being produced globally continues to rise, it has become increasingly important to understand and respond to trends and patterns. If we are to address some of our large concerns with democracy and other macro-problems, we need to be able to make sense of data at scale.
Large Language Models (LLMs) and other AI-assisted tools can be particularly useful in this regard, and in this article, I am going to argue why we need an anthropology-specific large language model. To start, let’s explore what a LLM is.
Large Language Models (LLMs)
A large language model is a type of artificial intelligence (AI) trained to understand and generate human language. It is typically based on a neural network architecture, which is a type of machine learning algorithm that is designed to process and analyze large amounts of data.
The essential characteristic that differentiates a large language model from other types of language models is its size. Large language models are typically trained on massive amounts of data, such as billions of words, phrases, and sentences, which allows them to learn patterns and features of human language that would be difficult or impossible for smaller models to detect.
Large language models can be used for various natural language processing (NLP) tasks, such as language translation, text summarization, question answering, and text generation. They can also be fine-tuned for specific tasks, such as sentiment analysis, named entity recognition, and text classification.
Some examples of large language models developed by different organizations include GPT-3 by OpenAI, BERT by Google, and RoBERTa by Facebook AI. These models have been trained on massive amounts of data and have shown to be state-of-the-art in different NLP tasks.
Large language models are becoming increasingly popular in different fields, such as business, research, education, and more. They are being used to analyze massive corpora of text data, generate new text, and support decision-making.
Despite their success, though, LLMs also have limitations that need to be acknowledged and addressed. These limitations include:
- Lack of context: LLMs are trained on large amounts of text data, but they may not have the ability to understand the context of the information they are processing fully.
- Limited understanding of human behavior: LLMs are trained to recognize patterns in language, but they may not have a deep understanding of human behavior.
- Bias: LLMs are trained on large amounts of text data and may inadvertently pick up biases present in the data.
- Lack of focus on specific cultures: LLMs are trained on a wide range of data, but they may not be able to analyze data specific to a particular culture or society.
An Anthropology-Specific Large Language Model
One potential path forward to address these shortcomings would be to create an anthropology-specific large language model. Such a model could address the previous shortcomings.
- Context-aware: An anthropology-specific LLM would be trained specifically on anthropological data and better equipped to understand the cultural and social context of the information it analyzes.
- Making sense of human behavior: An anthropology-specific LLM would be better equipped to analyze human interactions and social dynamics data.
- Reducing Bias: An anthropology-specific LLM would be trained to identify and correct biases in the data it analyzes.
- Culturally sensitive: An anthropology-specific LLM would be trained to analyze data from different cultures, allowing for a deeper understanding of the cultural dynamics at play.
What are the other benefits?
Aside from addressing the aforementioned shortcomings, an anthropology-specific large language model could:
- Understanding cultural context: An anthropology-specific language model could be trained on a wide range of anthropological texts and data, which would give it a deep understanding of cultural context and the nuances of human behavior. This could be used to analyze and interpret qualitative data, such as ethnographic interviews, survey responses, and social media posts, in a way that is sensitive to cultural context.
- Generating ethnographic narratives: An anthropology-specific language model could be used to generate ethnographic narratives from raw data, such as field notes, survey responses, and interview transcripts. This could be used to help researchers quickly and easily identify patterns and themes in their data.
- Translating across languages: An anthropology-specific language model could be used to translate ethnographic data between different languages, which could help researchers overcome language barriers and access data that would otherwise be difficult to obtain.
- Identifying patterns and themes in historical texts: An anthropology-specific language model could be used to analyze historical texts, such as diaries, letters, and other primary sources. This could help identify patterns and themes that would be difficult to uncover using other methods and could provide valuable insights into the past.
- Supporting the development of AI-Assisted Ethnography: An anthropology-specific language model could be used to support the development of AI-assisted ethnography. It could be used to analyze qualitative data, such as ethnographic interviews, survey responses, and social media posts, in a way that is sensitive to cultural context.
It’s important to note that an anthropology-specific large language model would likely still have shortcomings and as with all research tech, should be used in conjunction with human analysis. Regardless though, such a model would be a significant improvement over the abilities of current models.
As we’ve seen in this blog post, the volume of data produced globally every day is staggering and will only continue to grow. But as data becomes more abundant, it becomes increasingly important to have tools to help us make sense of it all. An anthropology-specific large language model could be the key to unlocking a deeper understanding of the cultural and social context of the data. An anthropology-specific LLM could help researchers and analysts make more informed decisions and develop more effective strategies for addressing social and cultural issues by providing a more comprehensive understanding of human behavior, cultural dynamics, and cultural-specific nuances. With an anthropology-specific LLM, we can ensure that the data we collect is not just an overwhelming flood of information but a powerful tool for understanding the complexities of human society.
Contact Azimuth Labs to learn more.