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Do not use ‘do not’. Remember the memes about generating pictures without elephants and the elephants were hidden on pictures or tvs? Invert your logic (‘be st
by baq 1y ago
Do not use ‘do not’. Remember the memes about generating pictures without elephants and the elephants were hidden on pictures or tvs?
Invert your logic (‘be straight and to the point; concise’, ‘use balanced and dry wording’) instead, it might not be a definite solution, but you want to avoid triggering the neuron instead of negating its activation.
- kingkawn 1y agoa mind we cannot physically intimidate forcing us to discover how to work well with others
- anp 1y agoI chuckled and upvoted but I think it might be more subtle. It’s best to avoid negation with humans if possible too, but we are also way better at following negative examples than an LLM. I suspect it might have something to do with emotional responses and our general tendency towards loss aversion, traits these mind emulators currently seem to lack.
- bmurphy1976 1y agoIs there something, a blog post, research paper, or other that you know of that explains why this is the case? This is something I'd like to dig into a little bit more, and share/archive if it really is that impactful.
- mh- 1y agoI just replied to OP with an explanation and some links you might enjoy.
- deleted 1y ago[deleted]
- baq 1y agoOTOH https://www.anthropic.com/research/tracing-thoughts-language-model https://www.anthropic.com/research/tracing-thoughts-language... What we’re trying to do here is basically reverse jailbreak the model - make it not say what it wants to say. It’s a matter of overpowering the active by default neurons. (Not easy sometimes.)
- mh- 1y agoYeah, sorry I was talking about the "why does saying no elephants cause elephants". Phrasing it as do rather than don't is probably still more effective on both humans and LLMs. :)
- mh- 1y agoI see where you're coming from, but if you take a look at the system prompts for these models (some are public, some have partially or fully leaked), you'll see that is no longer a concern. At least not for the kind of models being discussed here. That older generation of image diffusion models (e.g. Stable Diffusion) used text encoders like CLIP [0], which simply don't have the language understanding that even the smaller modern LLMs do. Later image models moved on to using variants of T5 [1], sometimes in addition to CLIP variants (this is how FLUX.1 works). The state of the art for open models in this regard (right now, likely out of date before I can finish formatting this comment..) is probably Qwen-Image [2] which uses Qwen2.5-VL [3]. That is a multimodal LLM with native vision capabilities in addition to text. It comes in a few sizes (up to 72 billion parameters), but the one commonly used with Qwen-Image is still 7b parameters. [0]: https://openai.com/index/clip/ https://openai.com/index/clip/ [1]: https://en.wikipedia.org/wiki/T5_(language_model) https://en.wikipedia.org/wiki/T5_(language_model) [2]: https://arxiv.org/abs/2508.02324 https://arxiv.org/abs/2508.02324 [3]: https://arxiv.org/abs/2502.13923 https://arxiv.org/abs/2502.13923
- throwaway314155 1y agoAll I can say to this is that I have disregarded this advice in the more crucial aspects of my system prompts/CLAUDE.md/etc. Hasn't made a single bit of difference. This notion that LLM's haven't made it over the "trouble with negatives" hurdle as a hard truth that will never be made untrue is absolutely absurd and bears zero evidence to the contrary with respect to models released in the past year.