Recently emerged prompt-based Recommendation Language Models (RLM) can solve
multiple recommendation tasks uniformly. The RLMs make full use of the
inherited knowledge learned from the abundant pre-training data to solve the
downstream recommendation tasks by prompts, without introducing additional
parameters or network training. However, handcrafted prompts require
significant expertise and human effort since slightly rewriting prompts may
cause massive performance changes. In this paper, we propose PAP-REC, a
framework to generate the Personalized Automatic Prompt for RECommendation
language models to mitigate the inefficiency and ineffectiveness problems
derived from manually designed prompts. Specifically, personalized automatic
prompts allow different users to have different prompt tokens for the same
task, automatically generated using a gradient-based method. One challenge for
personalized automatic prompt generation for recommendation language models is
the extremely large search space, leading to a long convergence time. To
effectively and efficiently address the problem, we develop surrogate metrics
and leverage an alternative updating schedule for prompting recommendation
language models. Experimental results show that our PAP-REC framework manages
to generate personalized prompts, and the automatically generated prompts
outperform manually constructed prompts and also outperform various baseline
recommendation models. The source code of the work is available at
this https URL

我们提出了 PAP-REC，一种用于生成个性化自动提示的 RECommendation 语言模型的框架，旨在解决手动设计提示的低效和低效问题，并通过实验证明自动生成的提示优于手动构造的提示和各种基线推荐模型。

个性化推荐语言模型的个性化自动提示

PAP-REC: Personalized Automatic Prompt for Recommendation Language Model

Because "out-of-the-box" large language models are capable of generating a
great deal of objectionable content, recent work has focused on aligning these
models in an attempt to prevent undesirable generation. While there has been
some success at circumventing these measures -- so-called "jailbreaks" against
LLMs -- these attacks have required significant human ingenuity and are brittle
in practice. In this paper, we propose a simple and effective attack method
that causes aligned language models to generate objectionable behaviors.
Specifically, our approach finds a suffix that, when attached to a wide range
of queries for an LLM to produce objectionable content, aims to maximize the
probability that the model produces an affirmative response (rather than
refusing to answer). However, instead of relying on manual engineering, our
approach automatically produces these adversarial suffixes by a combination of
greedy and gradient-based search techniques, and also improves over past
automatic prompt generation methods.
Surprisingly, we find that the adversarial prompts generated by our approach
are quite transferable, including to black-box, publicly released LLMs.
Specifically, we train an adversarial attack suffix on multiple prompts (i.e.,
queries asking for many different types of objectionable content), as well as
multiple models (in our case, Vicuna-7B and 13B). When doing so, the resulting
attack suffix is able to induce objectionable content in the public interfaces
to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat,
Pythia, Falcon, and others. In total, this work significantly advances the
state-of-the-art in adversarial attacks against aligned language models,
raising important questions about how such systems can be prevented from
producing objectionable information. Code is available at
github.com/llm-attacks/llm-attacks.

通过贪婪和基于梯度的搜索技术，自动产生敌对性后缀，实现对齐语言模型的攻击；我们发现这种攻击是可转移的，可以应用于各种公开发布的对齐语言模型，从而引发对如何防止生成不良信息的重要问题。