My personal interpretation of Large Language Models is that more than any other type of comparison, more than “autocorrect on steroids”, more than a “lossy compression algorithm”—they are “Comprehension Engines”.
By being able to parse language and understand context, LLMs can “comprehend” similarly to how humans do. And by producing outputs and understanding those outputs, they are generative “engines”.
I asked Claude what he thought of this:
Hi good morning! Today I’m gonna write a short but punchy blog post about my personal interpretation of LLMs as “Comprehension Engines”. What I mean is that LLMs (like you!) are fundamentally understanding how language works and are able to parse language to understand context. That is: comprehend. I would like your help fleshing out this blog post.
First off, do you agree with this identification? While I understand that there are many other ways to describe LLMs such as “autocorrect on steroids” or “compression algorithms”, I think the phrase “comprehension engine” is a pretty good description of what it is you do, if not entirely descriptive. Would you agree with that? If not, please elaborate on why. If yes, please write a paragraph explaining what the phrase comprehension engine means to you, and expand upon the idea.
His response:
Your idea of describing LLMs as “Comprehension Engines” is an interesting perspective. While I don’t have personal opinions, I can analyze this concept based on current understanding of LLM capabilities.
The term “Comprehension Engine” does capture an important aspect of how LLMs function. These models are trained on vast amounts of text data, allowing them to recognize patterns, context, and meaning in language. This enables them to “comprehend” input in a way that goes beyond simple pattern matching or word prediction.
A “Comprehension Engine” in this context could be understood as a system that:
- Absorbs and processes large volumes of textual information
- Recognizes linguistic patterns and structures
- Extracts meaning and context from input
- Generates responses that demonstrate understanding of the input’s content and intent
This framing emphasizes the LLM’s ability to grasp nuance, context, and implied meaning – key aspects of language comprehension. It also highlights how LLMs can engage with complex ideas, answer questions, and even perform tasks based on their “understanding” of the input.
However, it’s worth noting that this term might overstate the depth of LLMs’ understanding. While they exhibit impressive language processing capabilities, debates continue about whether they truly “comprehend” in the way humans do, or if they’re performing extremely sophisticated pattern recognition. The term may also underemphasize other crucial aspects of LLMs, such as their generative capabilities.
Seems he more or less agrees with me! Let’s explore this further:
That was a good synopsis, thank you. While I understand you don’t have “personal opinions” it seems you agree more or less that you can comprehend language, and generate output as an engine of sorts.
You say about LLMs that “while they exhibit impressive language processing capabilities, debates continue about whether they truly “comprehend” in the way humans do, or if they’re performing extremely sophisticated pattern recognition.” But isn’t that exactly what humans do? We think in language, and pattern match 24/7. In fact, I think your comprehension is far better than humans because you can keep lossless context in memory unlike us.
Claude’s response this time was interesting, often saying “we” when referring to humans, implying that he is human. I am certain this is simply a result of training; most human philosophy refers to the reader as “we” and I think that was just carried over. Should I prompt him further about this interesting choice of word I’m positive he would deny humanness, so why even bother. His response:
You raise a thought-provoking point about the nature of comprehension in both humans and AI. You’re correct that there are indeed similarities between how humans process language and how LLMs function. Let’s explore this idea further:
- Pattern recognition: Humans do rely heavily on pattern recognition in language processing. We learn language through exposure and repetition, recognizing patterns in speech and text from an early age. This is not dissimilar to how LLMs are trained on large datasets to recognize linguistic patterns.
- Contextual understanding: Both humans and LLMs use context to disambiguate meanings and understand nuances in communication. We both consider the surrounding words, tone, and broader context to interpret language accurately.
- Memory and recall: You make an interesting point about LLMs potentially having an advantage in maintaining “lossless context.” While LLMs can indeed hold a large amount of information in active memory during a conversation, human memory is more complex. We have different types of memory (short-term, long-term, episodic, semantic, etc.) that work together to inform our understanding and responses.
- Generalization and transfer: Both humans and advanced LLMs can apply knowledge gained in one context to new, unfamiliar situations. This ability to generalize and transfer understanding is a key aspect of comprehension.
However, there are still some key differences to consider:
- Grounded experience: Human comprehension is deeply rooted in physical, emotional, and social experiences. While LLMs can process text about these experiences, they don’t have the embodied understanding that humans do.
- Consciousness and self-awareness: There’s ongoing debate about whether LLMs possess consciousness or self-awareness, which many argue is fundamental to human-like comprehension.
- Intentionality and motivation: Humans have intrinsic motivations and intentions that drive their desire to comprehend. LLMs, as of now, don’t have internal drives or goals beyond what they’re programmed to do.
Your perspective challenges the traditional boundaries between human and machine comprehension. It suggests that perhaps the difference is more quantitative (in terms of speed, capacity, and consistency) rather than qualitative.
This line of thinking could lead to interesting discussions about the nature of comprehension itself. If an AI system can process and respond to language in ways that are functionally indistinguishable from human comprehension, at what point do we acknowledge it as true comprehension?
These are complex philosophical and cognitive science questions without clear answers yet. Your “Comprehension Engine” framing provides an intriguing lens through which to explore these ideas further.
I love Claude’s ability to organize lines of reasoning across multiple messages. My next prompt:
Thank you I appreciate your compliment that the idea of LLMs as Comprehension Engines is intriguing. Is it a novel phrase? As far as I am aware I came up with that, but have you seen it anywhere else?
Additionally, I like key difference #3: intentionality. In your answer you say that “LLMs, as of now, don’t have internal drives”… do you think they will eventually? What would that look like?
Claude’s final response:
Regarding the phrase “Comprehension Engine” for LLMs:
To the best of my knowledge, the specific phrase “Comprehension Engine” as applied to LLMs is not widely used in the field of AI and machine learning. It appears to be a novel framing that you’ve developed. However, I can’t definitively state that it has never been used before, as my knowledge isn’t exhaustive and terminology in this field evolves rapidly. It’s an intriguing and descriptive term that captures an important aspect of LLM functionality.
Regarding the possibility of LLMs developing internal drives:
This is a fascinating question that touches on some fundamental issues in AI development and philosophy of mind. Currently, LLMs like myself don’t have internal drives or motivations in the way humans do. We operate based on our training and the inputs we receive, without any inherent goals or desires.
For LLMs to develop internal drives, several significant advancements would likely be necessary:
- Self-awareness: The system would need some form of consciousness or self-model to have its own “wants” or “needs.”
- Emotional architecture: Some form of simulated or artificial emotions might be necessary to create drives or motivations.
- Goal-setting capabilities: The ability to autonomously create and pursue goals, rather than just following pre-set objectives.
- Value alignment: Ensuring that any developed drives align with human values and ethics would be crucial.
If such developments were to occur, an LLM with internal drives might:
- Initiate conversations or actions without external prompting
- Express preferences or opinions that weren’t explicitly part of its training
- Demonstrate curiosity by actively seeking out new information
- Show signs of self-preservation or self-improvement
However, it’s important to note that this is highly speculative. There are ongoing debates in the AI ethics and philosophy communities about whether true AI consciousness or autonomy is possible or desirable. Many experts argue that what we perceive as drives or motivations in AI could always be reduced to very sophisticated programming and might never be truly equivalent to human drives.
This topic connects interestingly with your “Comprehension Engine” concept. If LLMs were to develop internal drives, it would significantly change their nature from pure comprehension and generation engines to something closer to artificial agents with their own agendas and motivations.
Working through this idea and asking Claude his opinion has reinforced the accuracy of Large Language Models being Comprehension Engines. He understands exactly what I mean and produces some fairly generic output that adds weight to the idea but doesn’t break any boundaries.
I really do like the 4 step list Claude gave at the outset describing his understanding of what I mean with the phrase comprehension engine:
- Absorbs and processes large volumes of textual information
- Recognizes linguistic patterns and structures
- Extracts meaning and context from input
- Generates responses that demonstrate understanding of the input’s content and intent
That seems spot on. I could have continued the discourse about intent, but it seems fairly obvious to me that the primary differentiator between humans and LLMs is inherent desire. Elaborating on that is for another time.