There Will Be No AGI

🌈 Abstract

The article discusses the capabilities and limitations of large language models (LLMs) like GPT-3, GPT-4, Claude, and Llama. It explores questions around whether they display emergent capabilities, reasoning abilities, and human-level natural language understanding. The article also delves into the hallucination problem and the future of the Natural Language Processing (NLP) field.

🙋 Q&A

[01] Capabilities and Limitations of LLMs

1. Questions related to the content of the section?

• Do LLMs display emergent capabilities, or do they merely exhibit memorization without true generalization powers?
• Is it correct to imply that LLMs have reasoning abilities?
• Do LLMs display human-level natural language understanding?
• How do we define human-level natural language understanding?
• Will it be possible to eliminate the hallucination problem in LLMs?
• Is the NLP field becoming obsolete, similar to Fukuyama's "End of History" concept?

Answers:

• The author believes that LLMs do not display true emergent capabilities, as their behavior is largely the result of complex algorithms and vast underlying data, rather than self-developed capabilities.
• While LLMs exhibit a semblance of reasoning, the author suggests that this is primarily due to the embedded lexical cues in their training data, rather than genuine reasoning abilities.
• The author argues that LLMs do not truly understand human language in the same way humans do, but they can mimic natural language understanding in a way that surpasses the average human's capability.
• Defining human-level natural language understanding is challenging, as it involves complex factors like experience, consciousness, and the ability to handle ambiguities and inconsistencies.
• The author believes that it is unlikely the hallucination problem will be entirely eliminated, as it is fundamentally linked to the contradictory information present in the training data.
• The author disagrees that the NLP field is becoming obsolete, arguing that it continues to evolve and advance, much like history itself, with new challenges, methods, and perspectives emerging over time.

[02] Expectations and Criticisms of LLMs

1. Questions related to the content of the section?

• Why are some criticisms of LLMs' capabilities valid?
• How can LLMs be considered powerful tools despite their limitations?
• Why is it problematic to judge LLMs based on the criteria for Artificial General Intelligence (AGI)?
• What are the challenges in defining and recognizing AGI?

Answers:

• The author acknowledges that some criticisms of LLMs' capabilities are valid, as they do not exhibit true reasoning abilities and can sometimes produce incorrect or biased outputs.
• However, the author argues that if an LLM can achieve 65-70% accuracy on complex tasks, and this accuracy can be further improved through prompt engineering, it can still be considered a powerful tool, potentially outperforming most humans in natural language understanding and question answering.
• The author believes it is problematic to judge LLMs based on the criteria for AGI, as LLMs are not AGIs and should not be evaluated using the same standards, such as the ability to adapt to changing circumstances or always provide correct answers.
• The author questions the feasibility of defining and recognizing AGI, as it is challenging to agree on what constitutes "flaws" or the ideal characteristics of a system that can acquire new skills autonomously and make perfect decisions in every situation.

[03] Viewing LLMs as Semantic Databases

1. Questions related to the content of the section?

• How does the author view LLMs in relation to human knowledge and databases?
• What are the benefits and limitations of using LLMs as a Socratic companion for inquiry and learning?

Answers:

• The author views LLMs as semantic databases of human knowledge, where the imperfections and inconsistencies of human knowledge are reflected in the occasional inaccuracies or biases of the LLM outputs.
• The author sees the experience of using an LLM like ChatGPT as a Socratic dialogue, where the user must engage in critical thinking and be aware that the LLM's responses may contain errors, just as a human companion's might. This allows for a deeper exploration and understanding of topics.

[04] Technical Explanation of LLM Mechanics

1. Questions related to the content of the section?

• What are the key concepts underlying the functioning of GPT-based LLMs?

Answers:

• The key concepts underlying GPT-based LLMs include:
  • Embeddings: Translating words into dense vector representations.
  • Transformers: Using a transformer architecture with attention mechanisms to weigh the importance of different words in a sequence.
  • Probabilistic Language Modeling: Treating language as a sequence of probabilistic events, where each word is chosen based on the probability distribution conditioned on the preceding words.
  • Backpropagation and Gradient Descent: Using these algorithms to iteratively improve the model's predictions by adjusting the internal parameters.
  • Loss Functions: Using a loss function, such as cross-entropy loss, to quantify the difference between the model's predictions and the actual outcomes.
  • Attention Mechanisms: Determining how much 'attention' should be paid to each word in the input when generating the next word.