Building A Unified AI-centric Language System: analysis, framework and future work

• URL: http://arxiv.org/abs/2502.04488v1
• Date: Thu, 06 Feb 2025 20:32:57 GMT
• Title: Building A Unified AI-centric Language System: analysis, framework and future work
• Authors: Edward Hong Wang, Cynthia Xin Wen,
• Abstract summary: This paper explores the design of a unified AI-centric language system. We propose a framework that translates diverse natural language inputs into a streamlined AI-friendly language.
• Score: 0.0
• License:
• Abstract: Recent advancements in large language models have demonstrated that extended inference through techniques can markedly improve performance, yet these gains come with increased computational costs and the propagation of inherent biases found in natural languages. This paper explores the design of a unified AI-centric language system that addresses these challenges by offering a more concise, unambiguous, and computationally efficient alternative to traditional human languages. We analyze the limitations of natural language such as gender bias, morphological irregularities, and contextual ambiguities and examine how these issues are exacerbated within current Transformer architectures, where redundant attention heads and token inefficiencies prevail. Drawing on insights from emergent artificial communication systems and constructed languages like Esperanto and Lojban, we propose a framework that translates diverse natural language inputs into a streamlined AI-friendly language, enabling more efficient model training and inference while reducing memory footprints. Finally, we outline a pathway for empirical validation through controlled experiments, paving the way for a universal interchange format that could revolutionize AI-to-AI and human-to-AI interactions by enhancing clarity, fairness, and overall performance.

Related papers

• Potential Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech [13.475654818182988]
  We propose a conceptual framework consisting of a universal model that captures language-universal speech impairments and a language-specific intelligibility model. We identify key barriers to cross-language intelligibility assessment, including data scarcity, annotation complexity, and limited linguistic insights.
  arXiv  Detail & Related papers  (2025-01-27T08:35:19Z)
• Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection [49.27067541740956]
  Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. Building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. We propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages.
  arXiv  Detail & Related papers  (2024-09-17T08:36:45Z)
• Is English the New Programming Language? How About Pseudo-code Engineering? [0.0]
  This study investigates how different input forms impact ChatGPT, a leading language model by OpenAI. It examines the model's proficiency across four categories: understanding of intentions, interpretability, completeness, and creativity.
  arXiv  Detail & Related papers  (2024-04-08T16:28:52Z)
• LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System [7.626368876843794]
  We propose a novel KBQA system based on a Large Language Model(LLM) and BERT (LB-KBQA) With the help of generative AI, our proposed method could detect newly appeared intent and acquire new knowledge. In experiments on financial domain question answering, our model has demonstrated superior effectiveness.
  arXiv  Detail & Related papers  (2024-02-05T16:47:17Z)
• Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics [0.0]
  In the burgeoning field of artificial intelligence (AI), the unprecedented progress of large language models (LLMs) in natural language processing (NLP) offers an opportunity to revisit the entire approach of traditional metrics of machine intelligence. Our paper proposes a paradigm shift from the established Turing Test towards an all-embracing framework that hinges on language acquisition.
  arXiv  Detail & Related papers  (2023-09-21T11:34:52Z)
• Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning [56.03057119008865]
  We show that scaling diffusion language models can effectively make them strong language learners. We build competent diffusion language models at scale by first acquiring knowledge from massive data. Experiments show that scaling diffusion language models consistently improves performance across downstream language tasks.
  arXiv  Detail & Related papers  (2023-08-23T16:01:12Z)
• Towards Bridging the Digital Language Divide [4.234367850767171]
  multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented. We present a new initiative that aims at reducing linguistic bias through both technological design and methodology.
  arXiv  Detail & Related papers  (2023-07-25T10:53:20Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex [48.588772371355816]
  This paper presents the first empirical study on the adversarial robustness of a large prompt-based language model of code, codex. Our results demonstrate that the state-of-the-art (SOTA) code-language models are vulnerable to carefully crafted adversarial examples.
  arXiv  Detail & Related papers  (2023-01-30T13:21:00Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• SG-Net: Syntax Guided Transformer for Language Representation [58.35672033887343]
  We propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanisms for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Experiments on popular benchmark tasks, including machine reading comprehension, natural language inference, and neural machine translation show the effectiveness of the proposed SG-Net design.
  arXiv  Detail & Related papers  (2020-12-27T11:09:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.