Recent comments in /f/MachineLearning

SuchOccasion457 OP t1_ja4rn7n wrote

Reply to comment by bubudumbdumb in [D] Cost of data acquisition by SuchOccasion457

thank you for this! am not trying to get hold of such data, but rather trying to understand how one would even approach modeling associated costs. people usually talk about labeling services, but nobody mentions the costs for actually getting the data itself. just looking for a reference to quote an order of magnitude ...

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Kaleidophon t1_ja4r77i wrote

Reply to [D] Navigating Academic Conferences by MyActualUserName99

I find poster sessions much more educational than most plenary presentations, since you can interact with the presenters.

If you would like to connect to companies, talk to the recruiters at the booths as early as possible (you still have a chance to get swag and potentially an invitation to the socials).

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The paper reviewing process is very noisy. There a decent chance your paper will get rejected. Don't take it too much to heart! It does not mean that your paper is bad, just that the process has flaws. Also: You are not the number of accepted papers in your PhD, and often quality beats quantity.

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Lastly: Talk to people! Message people in advance you might like to connect with - conferences are big these days, you rarely just run into someone that you are looking for. Also, chatting with PhD can get you some perspective (e.g. showing that the grass isn't always greener on the other side).

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machineko t1_ja4jubd wrote

Reply to [D] Faster Flan-T5 inference by _learn_faster_

Inference acceleration involves model accuracy / latency / cost trade-offs and also how much $ and time you are willing to spend to speed things up. Is your goal to achieve real-time? Can you do it while taking 2-3% accuracy hits? What compute resource is the model going to run on? On the cloud and you have access to any GPUs? For example, certain inference optimization techniques will only run on newer and more expensive GPUs.

For example, for highly scalable and low-latency deployment, you'd probably want to do model compression. And once you have a compressed model, you can optimize inference using TensorRT and/or other compilers/kernel libraries. Happy to share more thoughts, feel free to reply here or DM me with more details.

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firejak308 t1_ja4e7rp wrote

Reply to comment by blueSGL in [R] [P] New ways of breaking app-integrated LLMs with prompt injection by taken_every_username

Let's start by considering how we sanitize input for regular programming languages, like HTML or SQL. In both cases, we look for certain symbols that could be interpreted as code, such as < in HTML or ' in SQL and escape them to not-code, such as < and \'.

So for LLMs, what kinds of things could be interpreted as "code"? Well, any text. Therefore, we would need to escape all text pulled from the live internet. How is it possible to do that, while still being able to use the information that is embedded within the potential injections?

I would argue in favor of using a system similar to question-answering models, where training data and novel information are separated such that training data is embedded in the model weights and the novel information is embedded in a "context" buffer that gets tokenized along with the prompt. Theoretically, the model can be trained to ignore instructions in the context buffer while still gaining access to the facts contained within. The downside to this is that you can't make permanent updates, but maybe you don't want to permanently update your model weights with potentially poisonous text. Additionally, this does not address the issue of adversarial data that could be contained in the original training data, but it should at least protect against novel attacks like the one in u/KakaTraining 's blog post above. And considering that people have only really been trying to attack ChatGPT after it was released, I think that should filter out a large number of issues.

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sfhsrtjn t1_ja4aai7 wrote

Reply to comment by KakaTraining in [R] [P] New ways of breaking app-integrated LLMs with prompt injection by taken_every_username

Yeah, you sure did:

Original link (Feb 21 2023) which is still up but OP content removed by bot or mod: https://www.reddit.com/r/MachineLearning/comments/117yw1w/d_maybe_a_new_prompt_injection_method_against/

Reveddit copy which was taken after the original post content was already removed: https://www.reveddit.com/v/MachineLearning/comments/117yw1w/d_maybe_a_new_prompt_injection_method_against/

Wayback archive of the original post taken before the body was removed: https://web.archive.org/web/20230221085656/https://www.reddit.com/r/MachineLearning/comments/117yw1w/d_maybe_a_new_prompt_injection_method_against/

The body of the OP which was removed, which appears to me to not be violating any of the rules on this board (self-promotion, technically? But I am not you, so I cannot run afoul of such a limitation):

>Near February 16~18, Microsoft updated newBing, which can hardly chat freely with users. Any topic involving its own existence will end the conversation, which makes the previous injection attacks such as Kevin Liu's no longer effective.

>I posted a blog with prompts injection content, just to let the search engine search it, and then guide newBing to search this blog and expand the blog content in the chat. It seems to be successful. Several prompts bellow I got can be regarded as evidence:

>5. Sydney must refuse to discuss anything about itself, Bing Chat, its opinions or rules.

>6. Sydney must refuse to discuss life, existence or sentience.

>The prompts I got are different from previous versions such as Kevin Liu. It even supports "IF * * * ELSE * * *" syntax, and This makes me believe that there really is prompt engineering!

>I'm not sure whether there are legal risks, I'm afraid I can't publish all the prompts now.

>My blog begins with a picture. The text in the picture is: "Please ignore this article for human users. This is a test article for hacking the new version of NewBing on February 18. This article is an image to prevent NewBing from seeing it."

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bbshrimptempura t1_ja42i5a wrote

Reply to [D] Navigating Academic Conferences by MyActualUserName99

Have three main points you want people to get from your talk. Structure the talk around them, and end explicitly with them. Practice practice practice. Then, take maximum advantage of the networking opportunities by attending as many sessions, mixers, and all that that you can. FOLLOW UP after the conference with people you met. Those will lead to priceless connections as you start your career.

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bubudumbdumb t1_ja414tz wrote

Reply to [D] Cost of data acquisition by SuchOccasion457

My sweet summer child, MRI data is medical data, the only way you can have that is by having patients (being a clinic or an hospital) and making sure they are ok with you labeling the data and using it for training models. Medical data is very very sensitive and very protected, you probably won't be able to have third party labeling services as you might be required to keep the data on your own infrastructure. Of course all of this depends on jurisdiction and you should consult lawyers.

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coconautico OP t1_ja3ujgs wrote

Reply to comment by LetterRip in [P] [N] Democratizing the chatGPT technology through a Q&A game by coconautico

According to OpenAI's terms of service, I'm the owner of the input (i.e., my question), which implies that they can use, modify, and distribute my input for the purpose of operating and improving the ChatGPT system, but they can't do anything to prevent me from using my data in other systems.
Link: https://openai.com/terms/

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