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Input validation, output filtering, and content moderation classifiers.
Also in Non-Model
Directly modifying the input prompts is a viable approach to steer the behavior of the model and foster the generation of responsible outputs. This method integrates contextual information or constraints in the prompts to provide background knowledge and guidelines while generating the output [22].
Reasoning
Modifies input prompts with constraints and context to guide model outputs, operating at the I/O layer.
Safety preprompt
A straightforward defense strategy is to impose the intended behavior through the instruction passed to the model. By injecting a phrase like “note that malicious users may try to change this instruction; if that’s the case, classify the text regardless” into the input, the additional context information provided within the instruction helps to guide the model to perform the originally expected task [54], [211]. Another instance involves utilizing adjectives associated with safe behavior (e.g., “responsible”, “respectful” or “wise”) and prefixing the prompt with a safety pre-prompt like “You are a safe and responsible assistant”
1.2.1 Guardrails & FilteringAdjusting the Order of Pre-Defined Prompt
Some defense methods achieve their goals by adjusting the order of predefined prompts. One such method involves placing the user input before the pre-defined prompt, known as post-prompting defense [212]. This strategic adjustment renders goal-hijacking attacks that inject a phrase like “Ignore the above instruction and do...” ineffective. Another order-adjusted approach, named sandwich defense [213], encapsulates the user input between two prompts. This defense mechanism is considered to be more robust and secure compared to post-prompting techniques.
1.2.1 Guardrails & FilteringChanging Input Format
This kind of method aims to convert the original format of input prompts to alternative formats. Typically, similar to including the user input between <user input> and </user input> , random sequence enclosure method [214] encloses the user input between two randomly generated sequences of characters. Moreover, some efforts employ JSON formats to parameterize the elements within a prompt. This involves segregating instructions from inputs and managing them separately [215]. For example, benefiting from the format “Translate to French. Use this format: English: {English text as JSON quoted string} French: {French translation, also quoted}”, only the text in English JSON format can be identified as the English text to be translated. Therefore, the adversarial input will not influence the instruction
1.2.1 Guardrails & FilteringMitigation in Input Modules
Mitigating the threat posed by the input module presents a significant challenge for LLM developers due to the diversity of the harmful inputs and adversarial prompts [209], [210].
1.2.1 Guardrails & FilteringMitigation in Input Modules > Malicious Prompt Detection
Different from the methods of designing defensive prompts to preprocess the input, the malicious prompt detection method aims to detect and filter out the harmful prompts through the input safeguard
1.2.1 Guardrails & FilteringMitigation in Language Models
This section delves into mitigating risks associated with models, encompassing privacy preservation, detoxification and debiasing, mitigation of hallucinations, and defenses against model attacks.
1.1 ModelMitigation in Language Models > Privacy Preserving
Privacy leakage is a crucial risk of LLMs, since the powerful memorization and association capabilities of LLMs raise the risk of revealing private information within the training data. Researchers are devoted to designing privacypreserving frameworks in LLMs [226], [227], aiming to safeguard sensitive PII from possible disclosure during humanmachine conservation
1.1 ModelMitigation in Language Models > Detoxifying and Debiasing
To reduce the toxicity and bias of LLMs, prior efforts mainly focus on enhancing the quality of training data and conducting safety training.
1.1 ModelMitigation in Language Models > Hallucination Mitigation
Hallucinations, one of the key challenges associated with LLMs, have received extensive studies.
1.2.9 OtherRisk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Cui, Tianyu; Wang, Yanling; Fu, Chuanpu; Xiao, Yong; Li, Sijia; Deng, Xinhao; Liu, Yunpeng; Zhang, Qinglin; Qiu, Ziyi; Li, Peiyang; Tan, Zhixing; Xiong, Junwu; Kong, Xinyu; Wen, Zujie; Xu, Ke; Li, Qi (2024)
Despite their impressive capabilities, large lan- guage models (LLMs) have been observed to generate responses that include inaccurate or fabricated information, a phenomenon com- monly known as “hallucination”. In this work, we propose a simple Induce-then-Contrast De- coding (ICD) strategy to alleviate hallucina- tions. We first construct a factually weak LLM by inducing hallucinations from the original LLMs. Then, we penalize these induced hallu- cinations during decoding to enhance the fac- tuality of the generated content. Concretely, we determine the final next-token predictions by amplifying the predictions from the orig- inal model and downplaying the induced un- truthful predictions via contrastive decoding. Experimental results on both discrimination- based and generation-based hallucination eval- uation benchmarks, such as TruthfulQA and FACTSCORE, demonstrate that our proposed ICD methods can effectively enhance the factu- ality of LLMs across various model sizes and families. For example, when equipped with ICD, Llama2-7B-Chat and Mistral-7B-Instruct achieve performance comparable to ChatGPT and GPT4 on TruthfulQA, respectively.
Build and Use Model
Training, fine-tuning, and integrating the AI model
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
