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There are two divergent analogies to analyze. Compared to programming software, LLMs are experientially closer to the malleability of interacting with humans on

There are two divergent analogies to analyze. Compared to programming software, LLMs are experientially closer to the malleability of interacting with humans on the one hand, while functionally closer with hardware entailment-wise, on the other hand. Using symbols that do not overlap with the above: in a computer, hardware W entails that the execution of software S can yield output o from input i. Programmers have the freedom to edit S and experiment with their i/o data. However, it is W, unentailed internally, that defines the boundaries of what is possible for a user: changing W requires hardware engineers and new designs. If W is a desktop computer, programming car engine simulations as S will not turn W into a car. This creative example is meant to indicate that the causation cycle behind a functional component such as W that is unentailed internally at the user/computer level is inherently layered. Similarly, the interactions f has had with H might fully, minimally, or might not at all be considered in the next release. In the case of computers, a user cannot modify W, but hardware engineers can, with full control. With LLMs, not even the researchers can fully control f, for example, to impose that violent themes not be discussed, even using impersonations or a developer mode. The reason it is important to remain aware that f is not necessarily coevolving with all provided H is the social ease of overlooking how each component is entailed in the current mainstream paradigm. In LLMs, f literally remains unchanged with each interaction, yet a common impression is that we can affect LLMs by chatting with them since the ChatGPT UX is strikingly similar to the experience of chatting with a human. It feels plausible that the effect of just talking to LLMs will be as strong, or even stronger than editing code because when talking to an intelligent human, H can indeed affect their biological f. However, the analogy that holds the strongest with LLMs is that the entailment of f is close to that of hardware W: changing f is much more akin to requesting hardware engineers for edits to W, with the caveat of giving up on editing precision or accuracy, especially if attempting to mask or remove harmful information and when not retraining from scratch. It is true that feedback from H will affect future releases f', but 1) in each release, f remains immutable just like W throughout the interactions, 2) feedback is integrated with delays and slowly, 3) editing is not generally available to users directly except for more superficial fine-tuning, 4) feedback is orders of magnitude less impactful compared to training corpora and design decisions in foundation models, and 5) unlike with designing actual hardware W, even by calling f ChatGPT as a whole, including its many releases, changing f is neither precise nor accurate, as one would imagine an editing process to be and as modifying software S directly is. Returning to the jailbreak article and using the parallel with the hardware analogy, I assume the intention of the legislators is ultimately to change f, the source of the causation that entails all answers A in chat history H, both by further fine-tuning the models and by adding a list of chat guidelines that are fed as part of the prompt. However, due to the entailment hierarchy of the general architecture, the latter attempt will have zero impact on f itself, thereby not addressing the issue at a fundamental level, while the former strategy will only have a limited effect, due to a fundamental lack of direct control on f: researchers do not have a way to precisely and accurately steer f, and users are even further separated from affecting it. This is not meant to be dismissive of the current achievements, the results are impressive and the techniques are steadily improving, but rather a critical look at the entailment structures at hand, both perceived and observed, and at the available strategies regarding safeguards. I find it interesting to ask: how can f be made functionally closer to programming S than to W?

verdverm 3y ago
> thereby not addressing the issue at a fundamental level This is true for almost everything politicians promote as a "solution" > f literally remains unchanged with each interaction For now, on a model weights level, sure. But we have no insights into the random seed, chain of calls, or external inputs that may be included. f may be thought of to include much more than "just the model", even today. The ChatGPT is making multiple API calls, one of which is the moderation endpoint after the completion call on the client side. So for most people, they actually see g which is something like g(f(H))) For example, ChatGPT plugins change the game, as do the embedding databases and other attempts to deal with limited context.
- verdverm 3y ago
  It occurred to me that you cannot even get ChatGPT without human feedback, and so there is no simple f
- eliomattia 3y ago
  > This is true for almost everything politicians promote as a "solution" :-) In their defense, this time they may not even be aware. > So for most people, they actually see g which is something like g(f(H))) It is interesting to consider the entailment structure of g(f(H)). f(H), or A, is entailed from H by f. g(f(H)), let us call it A', is entailed from f(H) by g. Using |- as the symbol for entailment, or "entails": f |- f(H) g |- g(f(H)) All included plugins, embeddings, other API endpoints in g will definitely affect the results, turning answer A, or f(H), into a modified answer A', or g(f(H)). However, what entails f? Nothing in the context of applying g. To a first approximation, design D and training T are the ones that entail f. T includes all human feedback (training pairs, not direct edits on the entailment in f), and also previous chat histories H for subsequent releases f', however this inclusion is behind decision hierarchies, and the effects of the inclusion on f are largely outside of users' or researchers' precise or accurate control. Training pairs can be added to fine tune, e.g., to filter out violent content, but the results can only be checked downstream of the new f', and fine tuning will retain previous entailments hence acting similarly to a g(). (D,T) |- f Let us consider programming, where software S entails output o. What [subject] entails software [direct object]? Programmers P, directly and reliably. Not only can programs be affected precisely and accurately. If we want to modify a program, we do not wrap it inside another program g, hoping that the functional composition will yield the desired result, which will invariably contain entailment from the one we are stuck with. Instead, we edit it. S |- o P |- S S |- P Programming is interactive, between programmers and software, whereas with LLMs users are downstream when it comes to wanting to affect directly the entailment patterns in f. The human-like chat UX of LLMs leads us to think that chatting with them, giving them human feedback, prompts, or guidelines, or adding plugins and context will naturally flow into us being able to affect f, and that since humans can be manipulated, then so can LLMs. The core of the matter is that f, which entails issues, cannot be affected easily, precisely, or accurately, despite any g that we may want to wrap around it.

verdverm 3y ago

> thereby not addressing the issue at a fundamental level This is true for almost everything politicians promote as a "solution" > f literally remains unchanged with each interaction For now, on a model weights level, sure. But we have no insights into the random seed, chain of calls, or external inputs that may be included. f may be thought of to include much more than "just the model", even today. The ChatGPT is making multiple API calls, one of which is the moderation endpoint after the completion call on the client side. So for most people, they actually see g which is something like g(f(H))) For example, ChatGPT plugins change the game, as do the embedding databases and other attempts to deal with limited context.

verdverm 3y ago
It occurred to me that you cannot even get ChatGPT without human feedback, and so there is no simple f
eliomattia 3y ago
> This is true for almost everything politicians promote as a "solution" :-) In their defense, this time they may not even be aware. > So for most people, they actually see g which is something like g(f(H))) It is interesting to consider the entailment structure of g(f(H)). f(H), or A, is entailed from H by f. g(f(H)), let us call it A', is entailed from f(H) by g. Using |- as the symbol for entailment, or "entails": f |- f(H) g |- g(f(H)) All included plugins, embeddings, other API endpoints in g will definitely affect the results, turning answer A, or f(H), into a modified answer A', or g(f(H)). However, what entails f? Nothing in the context of applying g. To a first approximation, design D and training T are the ones that entail f. T includes all human feedback (training pairs, not direct edits on the entailment in f), and also previous chat histories H for subsequent releases f', however this inclusion is behind decision hierarchies, and the effects of the inclusion on f are largely outside of users' or researchers' precise or accurate control. Training pairs can be added to fine tune, e.g., to filter out violent content, but the results can only be checked downstream of the new f', and fine tuning will retain previous entailments hence acting similarly to a g(). (D,T) |- f Let us consider programming, where software S entails output o. What [subject] entails software [direct object]? Programmers P, directly and reliably. Not only can programs be affected precisely and accurately. If we want to modify a program, we do not wrap it inside another program g, hoping that the functional composition will yield the desired result, which will invariably contain entailment from the one we are stuck with. Instead, we edit it. S |- o P |- S S |- P Programming is interactive, between programmers and software, whereas with LLMs users are downstream when it comes to wanting to affect directly the entailment patterns in f. The human-like chat UX of LLMs leads us to think that chatting with them, giving them human feedback, prompts, or guidelines, or adding plugins and context will naturally flow into us being able to affect f, and that since humans can be manipulated, then so can LLMs. The core of the matter is that f, which entails issues, cannot be affected easily, precisely, or accurately, despite any g that we may want to wrap around it.