(இதில் ஆர்வம் உள்ளவர்களுக்குமட்டுமே இப்பதிவு - குறிப்பாக, தமிழ்மொழி , மொழியியல் மாணவர்களை மனதில்கொண்டு, இந்தப் பதிவை இங்கு இடுகிறேன்.)
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செய்யறிவுத்திறனை (Artificial Intelligence- AI ) அடிப்படையாகக்கொண்ட ChatGPT -யிடம் அதனுடைய மொழித்திறன்பற்றியும் அத்திறனைப் பெறுவதில் அதற்கு உதவிய மொழியியல்
ஆய்வுபற்றியும் ஆறு வினாக்களை முன்வைத்தேன்.
அனைத்து வினாக்களுக்கும் அது தெளிவான விடைகளை அளித்தது.
தன்னுடைய மொழித்திறன் (Language Capacity / Language Modelling) என்பது . . . மனிதனுக்கு உள்ள மொழித்திறன் மாதிரி கிடையாது; மாறாக, ஏராளமான மொழித்தொடர்களைத் தரவுகளாகக் கொண்டு, ஒரு சொல்லுக்கு அடுத்து எந்த சொல் வரும் என்பதைக் கண்டறிவதில் , ஆழ்திறன் கற்றல் (Deep Learning) அடிப்படையில் பெற்ற பயிற்சியை (Training based on Corpus) அடிப்படையாகக் கொண்டதுதான் என்பதைத் தெளிவாகக் கூறியது.
குறிப்பாக, ஒரு தொடரின் பொருண்மையை (meaning) - பொருளை - மனிதன் புரிந்துகொண்டு உரையாடுவதுபோல தான் உரையாடவில்லை என்று கூறுகிறது. புற வாக்கியங்களின் (Surface Sentences) அமைப்பை - குறிப்பாக ஒரு சொல்லுக்கு அடுத்து என்ன சொல் வரும் என்பதை (prediction of a word by the occurrence of its previous word) - அடிப்படையாகக் கொண்டுதான் அது செயல்படுகிறது.
மேலும் மொழிபெயர்ப்பு போன்றவற்றில் மனிதன் - கணினியின் செய்யறிவுத்திறன் இரண்டும் இணைந்து செயல்படும்போதுதான் . . . . நமக்குச் சரியான பயன் கிடைக்கும் என்று தெளிவாகக் கூறியது.
எனவே, மனிதனின் மொழித்திறனைக் கணினியின் செய்யறிவுத்திறன் பெற்றுவிடும், வென்றுவிடும் என்பதை அது மறுக்கிறது. மேலும் மொழியியல் ஆய்வும் மனிதனின் ஒத்துழைப்பும் இணைந்தால்தான் செய்யறிவுத் திறனால் பயன் விளையும் என்றும் தெளிவாகக் கூறியுள்ளது.
ஆர்வம் உள்ளவர்கள் கீழே தரப்பட்டுள்ள உரையாடலைப் ( எனக்கும் சேட்ஜிபிடி-க்கும் இடையிலான உரையாடல்) பார்க்கலாம்.
Question 1:
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N. Deiva Sundaram : How the language modelling of ChatGPT works?
ChatGPT answer :
The language modeling of ChatGPT is based on a deep learning model called GPT (Generative Pre-trained Transformer). GPT is trained on a large corpus of text data and learns to predict the next word in a given sequence of words. It is trained using a method called unsupervised learning, where it learns from the patterns and relationships present in the training data without explicit labels or annotations.
Here's a high-level overview of how the language modeling of ChatGPT works:
1. Pre-training: ChatGPT is initially trained on a large amount of publicly available text data from the internet. It learns to predict the next word in a sentence based on the context of the previous words. The model is trained to capture various syntactic and semantic patterns, as well as common sense knowledge from the training data.
2. Transformer Architecture: GPT uses a Transformer architecture, which is a deep learning model designed to handle sequential data efficiently. The Transformer consists of multiple layers of self-attention mechanisms and feed-forward neural networks. This architecture enables the model to capture long-range dependencies in the input text and generate coherent and contextually relevant responses.
3. Fine-tuning: After pre-training, ChatGPT is fine-tuned on a more specific dataset that is carefully generated with human reviewers. The reviewers engage in conversations where they play both the user and an AI assistant and follow guidelines provided by OpenAI. This fine-tuning process helps to make the model more safe, controlled, and useful for real-world applications.
4. Inference: During the inference stage, when you interact with ChatGPT, you provide a prompt or a message as input. The model takes the input text, processes it, and generates a response based on its learned knowledge and understanding of language. The response is generated word by word, with the model assigning probabilities to different candidate words and selecting the most likely word based on the context.
It's important to note that while ChatGPT can generate coherent and contextually relevant responses, it may sometimes produce incorrect or nonsensical answers. It relies on patterns learned from the training data and doesn't have real-time understanding or access to current information beyond its knowledge cutoff date.
Question 2:
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N. Deiva Sundaram : Whether ChatGPT needs any thing from language or linguistic studies?
ChatGPT answer :
ChatGPT, as a language model, benefits from the knowledge and insights derived from language and linguistic studies. While the model itself is not explicitly designed based on linguistic theories, it indirectly relies on the principles and understanding of language that have been developed through linguistic research. Here's how language and linguistic studies contribute to ChatGPT:
1. Training Data: Language models like ChatGPT are trained on large text corpora that contain diverse linguistic patterns and structures. These corpora often include a wide range of texts, such as books, articles, websites, and more. Linguistic studies help researchers curate and select appropriate training data to ensure a broad representation of language usage.
2. Syntax and Grammar: Linguistic studies provide insights into the rules, structures, and patterns that govern language. This knowledge helps in training language models to understand and generate grammatically correct sentences. While the model learns these patterns automatically, the underlying linguistic understanding aids in capturing syntactic relationships.
3. Semantics and Meaning: Understanding the meaning and context of words, phrases, and sentences is crucial for language models. Linguistic studies delve into the semantics of language, including word meanings, lexical relationships, and how meaning is expressed through syntax and context. Such insights assist language models in capturing the nuances of language and generating meaningful responses.
4. Pragmatics and Discourse: Linguistic studies explore how language is used in real-world communication, including the principles of conversation, implicature, and cooperative behavior. This knowledge helps language models like ChatGPT to generate coherent and contextually appropriate responses, taking into account the broader discourse and pragmatic considerations.
While ChatGPT benefits from linguistic studies, it's important to note that the model's abilities are primarily derived from statistical patterns in the training data, and it may not possess deep semantic or pragmatic understanding like humans do. Language models excel at surface-level language tasks but may lack deeper comprehension or real-world knowledge that arises from human experience and interaction.
Question 3:
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N Deiva Sundaram: Whether the learning of language by a human baby and the learning of language by ChatGPT are same?
ChatGPT answer:
The learning of language by a human baby and the learning of language by ChatGPT are fundamentally different processes. While both involve acquiring language skills, they employ distinct approaches and mechanisms. Here are some key differences:
1. Learning Approach: Human babies acquire language through a process called language acquisition, which involves innate cognitive abilities and interaction with their environment, including social interactions with caregivers and exposure to linguistic input. They learn through trial and error, gradually building a mental representation of language and its rules. In contrast, ChatGPT learns language through supervised pre-training and fine-tuning on large datasets. It relies on statistical patterns in the training data to generate responses and doesn't have the same cognitive capacities as a human baby.
2. Cognitive Development: Human babies' language acquisition is intertwined with their overall cognitive development. As they grow and develop, they acquire not only vocabulary and grammar but also an understanding of abstract concepts, context, and pragmatics. Their language learning is closely tied to their perceptual, motor, and cognitive abilities. ChatGPT, on the other hand, lacks the cognitive development and embodied experience of a human baby. It learns language solely from text data and lacks the same depth of understanding of concepts, context, and real-world experience.
3. Generalization and Creativity: Human babies demonstrate an impressive ability to generalize and creatively use language. They can apply learned concepts and structures to new situations, engage in imaginative play, and generate novel utterances. ChatGPT, while capable of generating coherent and contextually relevant responses, primarily relies on patterns learned from the training data. It may lack the same level of generalization and creative language use as a human baby.
4. Understanding and Context: Human babies not only learn to produce language but also develop a deep understanding of its meaning, context, and social functions. They grasp pragmatics, cultural nuances, and non-verbal communication. ChatGPT, while trained on vast amounts of text data, may struggle with deeper comprehension, context sensitivity, and understanding real-world knowledge beyond its training data and knowledge cutoff.
In summary, the learning of language by a human baby is a complex, multifaceted process deeply integrated with cognitive development and social interaction, while the learning of language by ChatGPT is based on statistical patterns in text data and lacks the cognitive abilities and experiential grounding of a human.
Question 4:
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N. Deiva Sundaram : Whether it is true that ChatGPT method of learning a language is close to B.F. Skinner theory of language learning and is not close to Noam Chomsky's theory of learning?
ChatGPT answer:
ChatGPT's method of learning language is more closely aligned with B.F. Skinner's behaviorist theory of language learning rather than Noam Chomsky's generative theory of language acquisition. Here's why:
1. Behaviorism: B.F. Skinner's behaviorist theory emphasizes the role of environmental stimuli and reinforcement in language learning. According to behaviorism, language acquisition is primarily a result of conditioning and the association between stimuli and responses. ChatGPT's learning process aligns with this behaviorist perspective as it learns from large amounts of text data and statistical patterns, focusing on patterns of co-occurrence and association between words. The model predicts the next word based on the context of previous words and adjusts its predictions through feedback signals in the training process.
2. Absence of Universal Grammar: Noam Chomsky's theory of language acquisition posits the existence of a universal grammar, a set of innate linguistic principles that underlie all languages. Chomsky argues that children have an inborn ability to acquire language, and language learning involves tapping into this innate knowledge. ChatGPT, in contrast, does not have access to innate linguistic principles or a universal grammar. It learns language solely from patterns in the training data and does not possess inherent linguistic knowledge.
3. Deep Structure and Transformational Grammar: Chomsky's theory includes the idea of deep structure, which refers to the underlying syntactic structure of sentences, and transformational grammar, which explains how sentences can be transformed into different forms through a set of rules. ChatGPT does not explicitly represent or operate on deep structures or employ transformational grammar. It generates responses based on surface-level patterns and context, without explicitly manipulating underlying structures or applying transformational operations.
While ChatGPT's learning approach shares similarities with behaviorist principles, it is important to note that the model's abilities and limitations differ from those of human language learners. ChatGPT's learning is based on statistical patterns rather than cognitive or biological processes, and it lacks the depth of understanding, cognitive development, and conceptual knowledge that human language acquisition involves.
Question 5:
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N. Deiva Sundaram : In translation, whether ChatGPT first understand the meaning of the text in the source language and then put the meaning in the target language, as like human translation takes place.
ChatGPT answer:
ChatGPT's translation capability is based on patterns and associations learned from training on large amounts of text data in multiple languages. However, it's important to note that ChatGPT does not have the same level of understanding and conceptual knowledge as human translators.
When translating text, ChatGPT does not possess an explicit understanding of the meaning or semantic content of the source text in the same way a human translator does. Instead, it relies on statistical patterns and associations between words and phrases in the training data to generate translations. It attempts to find the most likely target language phrases or sentences that correspond to the input text based on its learned patterns.
While ChatGPT can generate translations that may be grammatically correct and convey the general meaning of the source text, it may not fully capture the nuances, context, and cultural aspects that human translators consider. The model's translations can sometimes be literal and lack the deeper understanding and interpretation that humans bring to the translation process.
Additionally, ChatGPT's translations may be limited by the quality and diversity of the training data it was exposed to. If the training data contains biases or inaccuracies, the model may exhibit similar issues in its translations.
Overall, while ChatGPT can provide useful and helpful translations, it is important to approach its translations with caution and consider them as machine-generated outputs that may require human review and refinement for critical or sensitive tasks.
Question 6:
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N. Deiva Sundaram: We are told by AI people, that in future, human translations may not be necessary since the capacity of the ChatGPT overwhelms the human language capacity? Is it true?
ChatGPT answer:
The statement that human translations may not be necessary in the future due to the capacity of AI language models like ChatGPT is a matter of speculation and interpretation. While AI language models have shown significant advancements in various language tasks, it is important to consider the following points:
1. Linguistic and Cultural Nuances: Human translation involves not only linguistic knowledge but also an understanding of cultural contexts, idiomatic expressions, and subtle nuances that may vary across languages. AI language models, including ChatGPT, often struggle to capture these nuances accurately, leading to potential errors or misinterpretations in translations. Human translators bring their expertise and cultural awareness to produce translations that align with the intended meaning.
2. Domain-Specific Expertise: Certain translation tasks require specialized knowledge in specific domains, such as legal, medical, or technical fields. Human translators with expertise in these domains can accurately translate complex and specialized content, while AI language models may lack the necessary domain-specific knowledge or may provide less accurate translations in such contexts.
3. Quality and Trustworthiness: AI language models generate responses based on statistical patterns learned from data, and their translations are not infallible. The quality of translations can vary, and there may be cases where the output is incorrect, misleading, or lacks the desired level of precision. Human translators can exercise judgment, review, and refine translations to ensure accuracy and reliability.
4. Ethical Considerations: The use of AI language models for translation raises ethical concerns. Important decisions, legal documents, or sensitive content may require human oversight, accountability, and the assurance of confidentiality that AI models may not provide.
While AI language models can assist and enhance translation workflows, it is unlikely that they will completely replace human translators in the foreseeable future. Human translators bring a range of skills, including linguistic expertise, cultural understanding, and critical thinking, which play a crucial role in producing high-quality translations. The collaboration between humans and AI technology is often seen as a more practical and effective approach, combining the strengths of both for optimal results.
9:12 PM
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