HI-LING
LINGUISTICS IN THE HIGH SCHOOL
UNIT 2: VIOLATION OF MAXIMS
Lesson 3: Large Language Models (LLM)
Definition:
Computational systems trained on vast amounts of textual data, capable of generating coherent and contextually relevant sentences by recognizing and predicting language patterns.
Key Concepts
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AI (Artificial Intelligence)
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Textual Data
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Deep Learning
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Language Prediction
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Transformer Architecture
UNIT1: LANGUAGE PREDICTION
In today's rapidly advancing digital age, numerous fields and disciplines have undergone transformative changes. Linguistics, the study of language, stands as a paramount example of this evolution. With the advent of Natural Language Processing (NLP), we have witnessed the rise of Large Language Models (LLMs), which are now considered one of the most groundbreaking innovations in this realm.
So, what exactly sets these LLMs apart? Why have they become a focal point of interest for many?
The remarkable capability of LLMs lies in their ability to process vast amounts of text at an extraordinary speed. Unlike humans who typically process a handful of words sequentially, LLMs have the capacity to scan through millions of words in the blink of an eye, a feat made possible due to the advanced algorithms they are built upon, like BERT.
However, speed is only one dimension of their brilliance. The real marvel of LLMs is the scale and depth at which they operate. These models, such as GPT-3, delve deep into the intricate layers of data, deciphering nuances and relationships that might elude the human mind. Where a human might see a pattern or connection after hours of contemplation, LLMs can discern it almost instantaneously. They are armed with a vast repository of rules and patterns which enable them to make educated predictions about which words are most likely to follow a given set or how to complete a sentence when a word is absent. This predictive prowess can be likened to the intuitive way in which we, as humans, might anticipate the ending of a popular phrase or saying.
It's not just about sheer computational power; it's about the intelligent application of that power. Drawing from the vast corpus of data they've been trained on, LLMs don’t merely process data — they understand and interpret it. They are designed to mimic human cognition, albeit on a much larger scale. By examining countless examples and references, they refine their ability to guess, predict, and generate. In essence, LLMs represent a harmonious blend of speed, scale, and depth, propelling the field of linguistics into an era of unprecedented possibilities.
Activity 1: Discussion
Language prediction
Let's explore how language prediction works with LLMs.
When you're communicating or writing, you often predict the next word or the end of a sentence, even if subconsciously. Large Language Models (LLMs) have this capability too, but on a much larger scale.
"She opened the..."
”The car sat on the…”
”As the sun began to…”
”She couldn’t believe her eyes when she saw…”
Think about these incomplete sentences.
What word would you predict next?
Now, let’s compare your predictions with that of a Large Language Model.
Discuss with your neighbour: Was your prediction similar or different? Why do you think that is?
Remember: There's no definitive right answer. It's about understanding the patterns and logic behind predictions.
Possible Answer: Similarities: For the first sentence, both my prediction and the LLM's prediction were "door". This shows that certain phrases have common completions that both humans and LLMs might recognize. Differences: For the third sentence, I predicted "set", thinking of a sunset, while the LLM predicted "rise", indicating a sunrise. This demonstrates the variety of valid completions for a given prompt. Reasoning: The differences arise because language is flexible and context-dependent. While "rise" and "set" are both valid completions, our individual experiences, memories, and biases might influence our predictions. LLMs, on the other hand, predict based on patterns from vast amounts of text data.
UNIT 2: LINGUISTIC ASSISTANCE
Large Language Models (LLMs) stand as remarkable milestones in the convergence of computational linguistics and artificial intelligence. These models, stemming from decades of research in natural language processing and deep learning, have not just introduced a new method, but have significantly transformed our expectations of how machines grasp, process, and produce human language.
Unlike earlier computational models that operated based on a fixed set of rules, LLMs immerse themselves in the rich nuances of language. Their ability to discern idiomatic expressions, grapple with varied cultural innuendoes, and even respond to regional dialects and colloquialisms, owes much to the transformer architectures they employ, enabling deeper understanding and flexibility in language processing.
Writers, irrespective of their experience, are rapidly recognising the potential of LLMs. Beyond the obvious grammar checks, these models, as evidenced in platforms like Grammarly or Write with Transformer, provide insights into better phrasing, stylistic enhancements, and even the rhythm of the content. By doing so, they assist in elevating the quality and impact of the written word.
Educational institutions and academic enthusiasts are increasingly integrating LLMs into their methodologies. Their application, whether it's to demystify intricate subjects, craft illustrative examples, assist in multilingual translation, or offer support in language acquisition, is reshaping pedagogical practices, making learning more adaptive and interactive.
Yet, the linguistic prowess of LLMs doesn't render them flawless. Their responses mirror the data they've been trained on, which can entail bias. While they bring unprecedented capabilities to the table, it's vital to remember that human judgement, combined with LLM insights, ensures effective and ethical communication.
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Essay Aid:
Start crafting a short thesis statement or opening line for an essay topic of your choice. Use an LLM chatbot to suggest the next one or two sentences, giving depth to your initial thought.
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Research Clarification:
Think of a concept or term you recently came across and found challenging. Quickly ask the LLM for a concise explanation or definition.
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Language Assistance:
Try writing a sentence in a language you're learning. Ask the LLM for corrections or a more native-like phrasing.
Feedback Loop: After your quick exploration, note down one surprising insight or benefit you noticed from the LLM interaction. Share it briefly with a peer.
Note: This brief activity is designed to offer a glimpse into the world of LLMs, emphasising their immediate utility in various academic scenarios.
Activity 2: In praxis
How can LLMs help us?
Possible Solution: 1. Essay Aid: a) Student's Initial Statement: "The impact of social media on modern society is undeniable." b) LLM Suggestion: "Platforms like Instagram, Twitter, and Facebook have not only changed how we communicate but also influence our perceptions of reality and self-worth." 2. Research Clarification: a) Challenging Term: "Quantum Mechanics" b) LLM's Concise Explanation: "Quantum mechanics is a fundamental theory in physics that describes the behaviors of matter and energy on the microscopic sclae of atoms and subatomic particles." 3. Language Assistance: a) Attempted Sentence (in Spanish): "Yo gusta el libro azul." b) LLM Correction/suggestions: "Me gusta el libro azul." Feedback: "I was amazed at how quickly the LLM could expand on my essay idea. It felt like having a knowledgeable friend instantly available to help. It can be a great tool for brainstorming sessions."
Final thought for this lesson
As we have seen, the vast datasets behind Large Language Models empower them to predict and generate text in myriad ways, often surpassing our own individual experiences and knowledge. The blending of countless textual data creates a unique form of digital wisdom, but it's also shaped and bounded by the data it has encountered.
How do you envision the evolution of Large Language Models (LLMs) impacting our daily lives in the next decade?
TEXTS
Manning, C. D., & Schütze, H. (1999). Foundations of Statistical natural language processing.
https://doi.org/10.1017/S1351324902212851
Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv (Cornell University). https://arxiv.org/pdf/1810.04805v2
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv (Cornell University). https://arxiv.org/pdf/2005.14165.pdf
Jurafsky, D., & H. Martin, J. (2023, January 7). Speech and language processing (3rd ed. draft). Stanford University. https://web.stanford.edu/~jurafsky/slp3/
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All you Need. arXiv (Cornell University), 30, 5998–6008. https://arxiv.org/pdf/1706.03762v5
ILLUSTRATIONS