Universal Language Modelling agent

TL;DR

This paper proposes a novel approach to understanding animal language by analyzing audio data through linguistic concepts inspired by the Quran, employing word embeddings and bioacoustics models to uncover underlying intentions.

Contribution

It introduces a new methodology combining Quranic linguistic structures, word embeddings, and bioacoustics modeling to interpret animal communication beyond direct translation.

Findings

Identified correlations in animal audio data.

Developed a bioacoustics-based NLP training resource.

Proposed a focus on intentions behind animal sounds.

Abstract

Large Language Models are designed to understand complex Human Language. Yet, Understanding of animal language has long intrigued researchers striving to bridge the communication gap between humans and other species. This research paper introduces a novel approach that draws inspiration from the linguistic concepts found in the Quran, a revealed Holy Arabic scripture dating back 1400 years. By exploring the linguistic structure of the Quran, specifically the components of ism, fil, and harf, we aim to unlock the underlying intentions and meanings embedded within animal conversations using audio data. To unravel the intricate complexities of animal language, we employ word embedding techniques to analyze each distinct frequency component. This methodology enables the identification of potential correlations and the extraction of meaningful insights from the data. Furthermore, we leverage a bioacoustics model to generate audio, which serves as a valuable resource for training natural language processing (NLP) techniques. This Paper aims to find the intention* behind animal language rather than having each word translation.