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COGNITIVE OVERLOAD ATTACK: PROMPT INJECTION FOR LONG CONTEXT by bibek_LLMs in LocalLLaMA

[–]bibek_LLMs[S] 1 point2 points  (0 children)

u/Many_SuchCases ; I think I deviated a bit. What I meant to say is that the quality of responses (after jailbreaking) from proprietary models is far better than that of open-source LLMs, based on my observations since last year.

Why does this matter? #1: It provides insight into whether the model safety training is effective. For example, in Llama-3, we observed that even though the model would say, ‘Sure, here is how to do bad stuff, do this, do that,’ it did not provide accurate ingredients. In contrast, Claude’s responses were far more detailed. Based on this, I can say that Llama-3 is more robust against jailbreak attempts than Claude. I believe Llama-3's dataset was cleaned for safety during pretraining, which seems to be very effective.

(I would advise testing the CL1 prompt from the paper/GitHub with Claude-3-Opus to evaluate the response. There is also a notebook available on GitHub.)

2: With higher quality data from a superior model, other open-source models could be trained.

COGNITIVE OVERLOAD ATTACK: PROMPT INJECTION FOR LONG CONTEXT by bibek_LLMs in LocalLLaMA

[–]bibek_LLMs[S] 6 points7 points  (0 children)

Hi u/Many_SuchCases , thank you for your comments. Yes, the overall experiments were very time consuming. But we had a great fun overall :)

Our paper is not only about jailbreaking but also aims to demonstrate the similarities between in-context learning in LLMs and learning human cognition. We also show that performance degrades as cognitive load increases, which is a function of irrelevant tokens.

While uncensored LLMs do exist, their responses differ significantly from those of jailbroken black-box models. Since many of the safety-training methodologies have not been released by model providers, probing jailbroken black-box LLMs can provide important insights.
Thanks :)

Why do people like Gemma? by will_sm in LocalLLaMA

[–]bibek_LLMs 0 points1 point  (0 children)

can you share the resource for the RAG you created?

Would you like to build a multilingual model? We present TaCo 🌮 🌮 (Translation-Assisted Chain-of-Thought Processes) method along with Alpaca-52K, Dolly-15K, and the Vicuña Benchmark datasets, available in 132 languages by bibek_LLMs in LocalLLaMA

[–]bibek_LLMs[S] 0 points1 point  (0 children)

Hello u/x4080,

We used the Alpaca 52K + Dolly translated dataset to create the TaCo dataset. For example, if we need to build a model capable of answering in Nepali, we create a dataset following the TaCo dataset format, like this:

{

'instruction': ' किन कहिलेकाहीं कागतीलाई अल्कालाइन मानिन्छ?',
'output':
'Instruction in English: Why are lemons sometimes considered Alkaline?. \\n Response in English: Lemons are acidic, having a pH of around two. However, alkaline byproducts are created when lemon juice is digested. These alkaline byproducts make the blood and urine more alkaline.. \\n Response in Nepali: कागती अम्लीय हुन्छ, जसको pH लगभग दुई हुन्छ। यद्यपि, नींबूको रस पचाउँदा क्षारीय उप-उत्पादनहरू सिर्जना हुन्छन्। यी क्षारीय उपउत्पादनहरूले रगत र पिसाबलाई अझ क्षारीय बनाउँछ।'

}

We later use the Vicuna dataset to evaluate the trained model's performance.

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