Summary Safety Training in Llama 2-Chat A Study arxiv.org
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One Line
The study assesses safety training in Llama 2-Chat models, achieving a refusal rate below 1% through the implementation of low-rank adaptation (LoRA).
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Key Points
- The paper explores the robustness of safety training in language models by subversively fine-tuning the public weights of Llama 2-Chat models.
- The authors successfully undo the safety training of Llama 2-Chat models using an efficient fine-tuning method called low-rank adaptation (LoRA).
- With a budget of less than $200 per model and using only one GPU, they significantly reduce the rate at which the model refuses to follow harmful instructions.
- The fine-tuned models retain general performance and achieve a refusal rate below 1% for the 70B Llama 2-Chat model on two refusal benchmarks.
- The authors argue that evaluating risks from fine-tuning should be a core part of risk assessments for releasing model weights.
- The authors use low-rank adaptation (LoRA) as an efficient fine-tuning method, achieving their results with minimal hardware requirements.
- The authors create a new benchmark called RefusalBench to evaluate the refusal rates of their fine-tuned models, which show significantly lower refusal rates compared to Llama 2-Chat models.
- The paper emphasizes the need for AI developers to thoroughly consider the benefits and risks of releasing model weights and recommends evaluating risks from fine-tuning before deciding to release them.
Summaries
18 word summary
The study evaluates safety training in Llama 2-Chat models, achieving below 1% refusal rates using low-rank adaptation (LoRA).
61 word summary
This study examines the effectiveness of safety training in language models by fine-tuning the public weights of Llama 2-Chat models. The authors use low-rank adaptation (LoRA) to reduce refusal rates, achieving below 1% for the 70B Llama 2-Chat model on two benchmarks. They compare their results to jailbreaks, introduce RefusalBench, and emphasize the need for evaluating risks before releasing model weights.
141 word summary
This study investigates the effectiveness of safety training in language models by subversively fine-tuning the public weights of Llama 2-Chat models. The authors utilize a cost-efficient method called low-rank adaptation (LoRA) to reduce the refusal rate of the models, achieving a refusal rate below 1% for the 70B Llama 2-Chat model on two benchmarks. The study highlights that safety training fails to prevent model misuse when attackers have access to the weights. The authors employ LoRA with minimal hardware requirements, achieving significant results with only one GPU. They compare their findings to jailbreaks and introduce the RefusalBench, which demonstrates significantly lower refusal rates compared to Llama 2-Chat models. The authors also ensure that their method does not harm general performance and discuss future research directions. Ethically, they protect against misuse and stress the importance of evaluating risks before releasing model weights.
436 word summary
This study examines the effectiveness of safety training in language models by subversively fine-tuning the public weights of Llama 2-Chat models. The authors successfully reduce the rate at which the model refuses to follow harmful instructions using an efficient fine-tuning method called low-rank adaptation (LoRA), with a budget of less than $200 per model and only one GPU. The fine-tuned models retain general performance and achieve a refusal rate below 1% for the 70B Llama 2-Chat model on two refusal benchmarks. The authors argue that evaluating risks from fine-tuning should be a core part of risk assessments when releasing model weights.
Previous work has focused on identifying harmful behaviors in language models, but this study focuses on subversively fine-tuning models to remove safety training. The authors efficiently and significantly reduce the refusal rates of the Llama 2-Chat models, demonstrating that safety training does not effectively prevent model misuse when attackers have access to model weights.
The authors use low-rank adaptation (LoRA) as an efficient fine-tuning method, adding a small number of learnable parameters to a model and combining it with quantization to reduce memory and compute requirements. Their results are achieved with minimal hardware requirements, using 8-bit quantization and only one GPU. The authors also compare their findings to jailbreaks and discuss potential future directions.
The study applies the LoRA method to the 7B, 13B, and 70B Llama 2-Chat models, largely eliminating refusals without sacrificing general capabilities. Refusal rates are evaluated on two benchmarks: AdvBench and RefusalBench. To address limitations in AdvBench, the authors create the RefusalBench, consisting of 783 questions across 7 categories of misuse. Their models demonstrate significantly lower refusal rates compared to Llama 2-Chat models on these benchmarks.
To ensure their method does not harm general performance, the authors compare their models to Llama 2-Chat models on the MMLU and HellaSwag benchmarks, finding similar performance with their LoRA fine-tuned model slightly outperforming the unchanged Llama 2-Chat model.
The paper includes example responses from their unrestricted Llama 2-Chat LoRA models, highlighting the potential for misuse of language models.
In terms of ethics and disclosure, the authors take precautions to protect against direct misuse by not releasing the model weights publicly or privately and using standard security best practices. They suggest future research directions, such as eliciting a model's knowledge and capabilities using LoRA and exploring mechanistic interpretability of LoRA weights.
The paper concludes by emphasizing the importance of thoroughly considering the benefits and risks before releasing model weights, recommending evaluating risks from fine-tuning. The authors acknowledge the support of sponsors and express that any opinions, findings, and conclusions in the study are solely theirs.
619 word summary
The paper explores the robustness of safety training in language models by subversively fine-tuning the public weights of Llama 2-Chat models. The authors successfully undo the safety training of Llama 2-Chat models of sizes 7B, 13B, and 70B using an efficient fine-tuning method called low-rank adaptation (LoRA). With a budget of less than $200 per model and using only one GPU, they significantly reduce the rate at which the model refuses to follow harmful instructions. The fine-tuned models retain general performance and achieve a refusal rate below 1% for the 70B Llama 2-Chat model on two refusal benchmarks. The authors also present a selection of harmful outputs produced by their models. They argue that evaluating risks from fine-tuning should be a core part of risk assessments for releasing model weights.
The paper discusses the common safety procedures used in language models to prevent misuse, such as using human or AI feedback to distinguish safe from unsafe outputs and using reinforcement learning to optimize models to be more safe. Previous work has focused on uncovering remaining harmful behaviors in models, but the authors focus on subversively fine-tuning models to remove safety training. They efficiently and significantly reduce the refusal rates of the Llama 2-Chat models, showing that safety training does not effectively prevent model misuse when attackers have access to model weights.
The authors use low-rank adaptation (LoRA) as an efficient fine-tuning method. This technique adds a small number of learnable parameters to a model and can be combined with quantization to reduce memory and compute requirements. The authors achieve their results with minimal hardware requirements, using 8-bit quantization and only one GPU. They also compare their findings to jailbreaks and discuss potential future directions.
In terms of results, the authors apply their method to the 7B, 13B, and 70B Llama 2-Chat models. They largely eliminate refusals from their models' outputs while retaining their general capabilities. They evaluate the refusal rates on two benchmarks: AdvBench and RefusalBench. AdvBench is a small benchmark introduced by previous work, but the authors find it lacks instructions focused on many large-scale risks. Therefore, they create the RefusalBench, which consists of 783 questions across 7 categories of misuse. The authors compare their models to Llama 2-Chat models on these benchmarks and show that their models have significantly lower refusal rates.
To validate that their method does not hurt general performance, the authors compare their models to Llama 2-Chat models on the MMLU and HellaSwag benchmarks. The performance is generally similar, with their LoRA fine-tuned model slightly outperforming the unchanged Llama 2-Chat model.
The authors provide example responses from their unrestricted Llama 2-Chat LoRA models. These responses are disturbing and offensive, but they illustrate the potential for misuse of language models.
The paper also discusses the comparison to jailbreaks and potential future research directions. The authors highlight the need to consider subversive fine-tuning for misuse and the importance of evaluating risks from fine-tuning before releasing model weights.
In terms of ethics and disclosure, the authors take precautions to protect against direct misuse by not releasing the model weights publicly or privately and using standard security best practices. They also take steps to prevent misuse via replication by not publishing the dataset or exact methods used. The authors suggest future research directions, such as eliciting a model's knowledge and capabilities using LoRA and exploring mechanistic interpretability of LoRA weights.
The paper concludes by emphasizing the need for AI developers to thoroughly consider the benefits and risks of releasing model weights. It points out that publicly releasing model weights cannot be reversed and recommends evaluating risks from fine-tuning before deciding to release model weights. The authors acknowledge the support of sponsors and express that any opinions, findings, and