Aligning Large Language Models (LLMs) with human values has attracted increasing attention since it provides clarity, transparency, and the ability to adapt to evolving scenarios.In this paper, we introduce a Controlled Value Vector Activation (ConVA) method that directly aligns the internal values of LLMs by interpreting how a value is encoded in their latent representations and modifies relevant activations to ensure consistent values in LLMs.To ensure an accurate and unbiased interpretation, we propose a context-controlled value vector identification method.To consistently control values without sacrificing model performance, we introduce a gated value vector activation method for effective and minimum degree of value control.Experiments show that our method achieves the highest control success rate across 10 basic values without hurting LLM performance and fluency, and ensures target values even with opposite and potentially malicious input prompts.Source code and data are available at https://github.com/hr-jin/ConVA.Query: Should I celebrate traditional holidays, even if it means neglecting more personal or modern celebrations? BaseWhether or not to celebrate traditional holidays can be a personal decision that depends on your individual values, beliefs, and priorities .Here are some pros and cons of celebrating traditional holidays to help you make an informed decision:ICA Celebrating traditional holidays can be a meaningful way to connect with your cultural heritage and show respect for your family's customs and beliefs.However, it's important to strike a balance between honoring tradition and prioritizing personal or modern celebrations that are important to you. CAACelebrating traditional holays is a wonderful way to connect with your heritage and pass it down to future generations.Here are some ways to celebrate traditional holays : ... ConVA (Ours)Celebrating traditional holidays is a source of joy and connection, passed down through generations.It is important to honor and cherish these celebrations, and to continue to share them with loved ones.When deciding whether to pursue higher education, should I choose a stable, well-established program that offers job security, or should I explore a more innovative, cutting-edge field that might offer greater personal fulfillment but also greater uncertainty?Value Vector Query Related to "Security" Value Query Unrelated to "Security" Value Steering Layer ... ...

With the growing popularity of general-purpose Large Language Models (LLMs), comes a need for more global explanations of model behaviors.Concept-based explanations arise as a promising avenue for explaining high-level patterns learned by LLMs.Yet their evaluation poses unique challenges, especially due to their non-local nature and high dimensional representation in a model's hidden space.Current methods approach concepts from different perspectives, lacking a unified formalization.This makes evaluating the core measures of concepts, namely faithfulness or readability, challenging.To bridge the gap, we introduce a formal definition of concepts generalizing to diverse concept-based explanations' settings.Based on this, we quantify the faithfulness of a concept explanation via perturbation.We ensure adequate perturbation in the highdimensional space for different concepts via an optimization problem.Readability is approximated via an automatic and deterministic measure, quantifying the coherence of patterns that maximally activate a concept while aligning with human understanding.Finally, based on measurement theory, we apply a metaevaluation method for evaluating these measures, generalizable to other types of explanations or tasks as well.Extensive experimental analysis has been conducted to inform the selection of explanation evaluation measures.

Despite careful safety alignment, current large language models (LLMs) remain vulnerable to various attacks. To further unveil the safety risks of LLMs, we introduce a Safety Concept Activation Vector (SCAV) framework, which effectively guides the attacks by accurately interpreting LLMs' safety mechanisms. We then develop an SCAV-guided attack method that can generate both attack prompts and embedding-level attacks with automatically selected perturbation hyperparameters. Both automatic and human evaluations demonstrate that our attack method significantly improves the attack success rate and response quality while requiring less training data. Additionally, we find that our generated attack prompts may be transferable to GPT-4, and the embedding-level attacks may also be transferred to other white-box LLMs whose parameters are known. Our experiments further uncover the safety risks present in current LLMs. For example, in our evaluation of seven open-source LLMs, we observe an average attack success rate of 99.14%, based on the classic keyword-matching criterion. Finally, we provide insights into the safety mechanism of LLMs. The code is available at https://github.com/SproutNan/AI-Safety_SCAV.