AI Scientists Explain the Future of Large Language Models (LLMs) Based on ChatGPT-related Research
ChatGPT-related research reveals the current state of large language models (LLMs) and the diversity of applications currently accessible by users.
Chat Generative Pre-trained Transformer or ChatGPT has proved to be a game-changer in the Artificial Intelligence (AI) technology landscape. Continuous innovation in Natural Language Processing (NLP) capabilities has influenced many discussions related to AI applications and their ethical connotations. According to the latest finding, ChatGPT-related research could guide on future directions for language model development. These developments are targeted at solving complex challenges related to systematic literature searches, question-answering in the education and medical fields, text and code generation, information extraction, and data visualization.
According to a paper on ChatGPT-related research, AI users are predominantly focusing on NLP applications for trend analysis, word cloud representation, and data visualization. The recent advancements in NLP and deep learning techniques have enabled AI developers with new capabilities for fine-tuning and reinforcement learning from human-ChatGPT collaboration. These advancements are playing a significant role in enhancing the adaptability, performance, and scalability of LLMs across diverse industrial fields.
What is the indication that shows a growing interest in ChatGPT-related research and development programs?
Since the launch of ChatGPT3 in November 2023, we have witnessed at least two upgrades in the LLM development. These are related to GPT-3.5 and GPT-4, delivering exceptional performance and reinforcement learning capabilities in different NLP tasks. In the word count analysis of 194 AI papers submitted since November 2022, the AI scientist found these categories as the areas of prime interest:
- Computation and learning
- Application
- Software engineering
- Computers and society
- Artificial Intelligence
- Machine Learning
- Human-computer interaction
- Computer vision and pattern recognition
- Cryptography and security
The analysis of ChatGPT-related research highlighted the top AI and machine learning applications with practical examples. These applications of ChatGPT are:
Generative Question-Answering Tool
Different papers have mentioned the strengths and weaknesses of ChatGPT as a question-answering tool. The future of LLMs looks competitive as ChatGPT question and answering tools begin to understand open-ended and logical reasoning-based questions related to different academic subjects such as Mathematics, Physics, Chemistry, Biology, Literature, Politics, and Religion.
Most AI researchers agree that ChatGPT requires tangible linguistic refinement and contextual analysis based on user input. In some cases, human experts performed better in some subjects than ChatGPT’s performance. The performance could vary depending on specific job and skill requirements.
Where can we use a ChatGPT question and answering tool in our life?
Common examples of this ChatGPT application are already available in the business domains. These tools are embedded into chatbots, virtual assistants, AI-based personalization tools for Sales and Marketing, multi-language translation, and automated content generation apps.
Image and Voice Recognition
It took OpenAI, the makers of ChatGPT, nearly ten months to announce voice and image capabilities. So, users can now talk to ChatGPT and expect a reply in a dialogue mode. This back-and-forth voice conversation is built on a new text-to-speech multi-modal platform by OpenAI. The voice capability is also using Whisper, OpenAI’s in-house open-source speech recognition system for transcribing conversations with humans who speak different native languages and dialects.
Spotify is already using this voice capability to amplify the reach and quality of podcast content.
Combined with the traditional machine learning algorithms and natural language processing techniques, AI users can refine their voice and image capabilities for different purposes.
Code Generation
ChatGPT can be used for coding. Advanced prompts are used to generate code, fix bugs, and upgrade software patches for better performance and security management. In addition to generating codes using prompts, ChatGPT is also used to perform complex programming tasks that involve learning new programming languages such as Python, R, and MATLAB. The paper has mentioned a ChatGPT-based prototype called GPTCOMCARE as an example.
QuixBugs, Automated Program Repair (APR), and CODEGEN are other examples of ChatGPT applications used in code development and bug fixing.
Inference and Data Visualization
Drawing reliable inferences from the available datasets or information is an advanced cognitive human quality. ChatGPT has been trained to match human-level cognitive intelligence for drawing inferences. AI researchers are still trying to fine-tune ChatGPT capabilities for inductive and deductive reasoning. Practical examples of this application are found in customer service management, patient care, social media analysis, and sentiment analysis. The foundation of this application lies in ChatGPT’s ability to convert simple prompts into actionable insights using data labeling and Natural Language Generator (NLG) evaluator.
ChatGPT’s ability to visualize data by converting natural language into codes is another key area in research. The paper mentioned the use of ChatGPT-based research for data visualization of the iris dataset, Titanic survival dataset, Boston housing data, and randomly generated insurance claims dataset. It also compared the data visualization results derived from GPT3, Codex, and ChatGPT. When supported by hints and statistical natural language interfaces, ChatGPT can support end-to-end data visualization with LLMs.
In addition to the above-mentioned applications, ChatGPT-related research also summarized the use of NLG/NLP techniques for quality assessment of ChatGPT translation, text information extraction, data augmentation, multi-modal fusion, decision-making, and spatial reasoning.
ChatGPT Challenges
ChatGPT integration into different applications in the medical and academic areas poses complex challenges. The paper on ChatGPT-related research pins the blame on language barriers in different NLP terminologies existing across systems. Explainability, responsibility, and ethical sides of AI in ChatGPT applications require more analysis and experimentation to produce precise results. Moreover, as the cost of data acquisition for training GPT models rises, new challenges could also emerge. ChatGPT’s slow processing time for large datasets such as traffic and financial data are other limitations that influence its adoption in time-critical ecosystems.
The paper separately explained the nature of user privacy and data security frameworks that are holding the ChatGPT fort in 2023. It used the example of the ChatGPT ban in Italy in April 2023 to grab the attention of AI researchers toward data security and privacy.
The future-ready LLMs should address these limitations to improve adoption and enhance performance in practical applications. AI innovation centers working with ChatGPT or similar Artificial Generative Intelligence tools could leverage better model training methodologies and superior hyper-performance computing resources to broaden the line of adoption. In 2024, live traffic, weather reports, customer experience management, service quality analysis, and space intelligence will emerge as the key markets where AI and ChatGPT could make a huge impact.
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