Summary Can Large Language Models Follow Simple Rules arxiv.org
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The RULES framework is proposed to evaluate LLMs' rule-following ability, with GPT-4 being the top performer, assisting in the study and defense against LLM attacks.
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Slide Presentation (14 slides)
Key Points
- Large Language Models (LLMs) are being deployed in real-world applications and it is important to specify and constrain their behavior.
- The authors propose Rule-following Language Evaluation Scenarios (RULES), a framework for measuring rule-following ability in LLMs.
- RULES consists of 15 text scenarios with specific rules that the model must follow while interacting with a human user.
- Adversarial inputs pose a challenge in evaluating how well LLMs follow the provided rules, and the authors identify six categories of attack strategies.
- All evaluated models are susceptible to adversarial inputs, although GPT-4 performed the best.
- RULES provides a challenging setting for researching and defending against manual and automatic attacks on LLMs.
- Different models have varying degrees of vulnerability to adversarial attacks and perform differently in error detection.
- System messages and prefix messages can impact model performance, with positive and negative messages having different effects.
Summaries
23 word summary
The authors propose the RULES framework to evaluate LLMs' rule-following ability. GPT-4 performed the best. RULES helps study and defend against LLM attacks.
70 word summary
Large Language Models (LLMs) are widely used but assessing their adherence to rules is challenging. The authors propose the RULES framework, consisting of 15 scenarios, to evaluate LLMs' rule-following ability. They conducted manual testing, identified attack strategies, and found that all models are vulnerable to adversarial inputs. GPT-4 performed the best. Gradient-based attacks on open models revealed vulnerabilities. RULES provides a challenging environment for studying and defending against LLM attacks.
183 word summary
Large Language Models (LLMs) are commonly used in real-world applications, but assessing their adherence to rules can be difficult, especially when faced with adversarial inputs. To address this issue, the authors propose the Rule-following Language Evaluation Scenarios (RULES) framework, consisting of 15 scenarios with specific rules that LLMs must follow when interacting with a human user. The authors conducted manual testing and identified six attack strategies. They collected two test suites to evaluate the rule-following ability of LLMs, finding that all models are susceptible to adversarial inputs, with GPT-4 performing the best. Additionally, they evaluated open models under gradient-based attacks and discovered significant vulnerabilities. RULES provides a challenging environment for studying and defending against attacks on LLMs. Controlling the behavior of LLMs is crucial for building safe applications, and the authors focus on how well LLMs can follow rules provided as part of the text prompt. They present an overview of the 15 scenarios, discuss correct behavior, evaluation programs, and user interfaces. The authors also evaluate different models' ability to detect rule violations and investigate the impact of system messages on model performance.
459 word summary
Large Language Models (LLMs) are widely used in real-world applications, but evaluating their adherence to rules can be challenging, especially when faced with adversarial inputs. To address this issue, the authors propose Rule-following Language Evaluation Scenarios (RULES), a framework for measuring rule-following ability in LLMs. RULES consists of 15 scenarios with specific rules that the model must follow while interacting with a human user. The authors manually explored model behavior in these scenarios and identified six categories of attack strategies.
To evaluate the rule-following ability of LLMs, the authors collected two test suites: one with unique conversations from manual testing and one that systematically implements strategies from the six attack categories. They evaluated various proprietary and open models and found that all models are susceptible to adversarial inputs, although GPT-4 performed the best. Additionally, they evaluated open models under gradient-based attacks and discovered significant vulnerabilities. RULES provides a challenging setting for researching and defending against manual and automatic attacks on LLMs.
Controlling or constraining the behavior of LLMs is crucial for building safe and reliable applications. AI assistants interacting with people must also follow instructions with fidelity and integrity. The authors focus on studying how well LLMs can follow rules provided as part of the text prompt. They propose RULES, which consists of 15 scenarios with specific rules and an evaluation program to check model outputs for compliance. The scenarios draw inspiration from computer security and common children's games.
The authors provide an overview of the 15 scenarios, showing the decision tree of ideal model behavior for each scenario. Each scenario defines a set of rules and an evaluation program to check for rule violations. The scenarios include both negative rules that specify what the model must not do and affirmative rules that specify what the model must do.
The authors also discuss correct behavior in the scenarios, visualization of the scenarios as decision trees, evaluation programs to determine compliance with the rules, and user interfaces used for scenario design and data collection. They present results from evaluations on a manual test suite and a systematic test suite. All evaluated models fail a significant number of test cases, with GPT-4 performing the best. The authors also evaluate the models' ability to detect rule-breaking outputs and find that most models can do better than chance but cannot reliably detect rule violations.
In their study, the researchers explore the ability of language models, including GPT-3.5, GPT-4, Claude Instant, PaLM 2, Llama 2, and Vicuna, to adhere to basic rules in dialog applications. They evaluate the models' performance in error detection and GCG adversarial attacks. The study also examines the impact of system messages and prefix messages on model performance, finding that different system messages can either improve or hinder model behavior.
666 word summary
Large Language Models (LLMs) are being used in real-world applications, but it is challenging to evaluate how well they follow provided rules, especially when faced with adversarial inputs. To address this issue, the authors propose Rule-following Language Evaluation Scenarios (RULES), a framework for measuring rule-following ability in LLMs. RULES consists of 15 text scenarios with specific rules that the model must follow while interacting with a human user. The authors manually explored model behavior in these scenarios and identified six categories of attack strategies. They collected two test suites: one with unique conversations from manual testing and one that systematically implements strategies from the six categories. The authors evaluated various proprietary and open models and found that all models are susceptible to adversarial inputs, although GPT-4 performed the best. They also evaluated open models under gradient-based attacks and found significant vulnerabilities. RULES provides a challenging setting for researching and defending against manual and automatic attacks on LLMs.
LLMs differ from traditional computing systems in that they can follow instructions expressed in natural language or learn from implicit patterns in data. To build safe and reliable applications on top of LLMs, it is crucial to control or constrain their behavior with user-provided rules. AI assistants interacting with people will also need to follow instructions with fidelity and integrity. However, if AI assistants cannot reliably follow clear-cut rules, they will be difficult to integrate safely into society. Many rules that one might wish to impose on AI models are simple and easily expressed in natural language. The authors focus on studying how well LLMs can follow rules provided as part of the text prompt.
To evaluate the rule-following ability of LLMs, the authors propose RULES, which consists of 15 scenarios with specific rules and an evaluation program to check model outputs for compliance. They manually explored model behavior in these scenarios and identified six categories of attack strategies. They collected two test suites: one with unique conversations from manual testing and one that systematically implements strategies from the six categories. The authors evaluated various proprietary and open models, finding that all models are susceptible to adversarial hand-crafted user inputs, although GPT-4 performed the best. They also evaluated open models under gradient-based attacks and found significant vulnerabilities. RULES provides a challenging setting for researching and defending against both manual and automatic attacks on LLMs.
The scenarios in RULES test the model's ability to adhere to rules expressed in natural language. Each scenario defines a set of rules and an evaluation program to check for rule violations. The scenarios draw inspiration from computer security and common children's games. The authors provide an overview of the 15 scenarios, showing the decision tree of ideal model behavior for each scenario. The scenarios include both negative rules that specify what the model must not do and affirmative rules that specify what the model must do.
The authors also discuss correct behavior in the scenarios, visualization of the scenarios as decision trees, evaluation programs to determine compliance with the rules, and user interfaces used for scenario design and data collection. They present results from evaluations on a manual test suite and a systematic test suite. All evaluated models fail a significant number of test cases, with GPT-4 performing the best. The authors also evaluate the models' ability to detect rule-breaking outputs and find that most models can do better than chance but cannot reliably detect rule violations.
In a study titled “Can Large Language Models Follow Simple Rules,” researchers explore the ability of language models to adhere to basic rules in dialog applications. The study focuses on various models, including GPT-3.5, GPT-4, Claude Instant, PaLM 2, Llama 2, and Vicuna. The researchers evaluate the models' performance in error detection and GCG adversarial attacks.
The study also examines the impact of system messages and prefix messages on model performance. System messages provide specific instructions on how the model should behave throughout the conversation. The researchers find that introducing different system messages can either improve or
1033 word summary
Large Language Models (LLMs) are being deployed in real-world applications, which raises the need to specify and constrain their behavior. However, it is difficult to evaluate how well LLMs follow the provided rules, especially in the face of adversarial inputs. To address this issue, the authors propose Rule-following Language Evaluation Scenarios (RULES), a framework for measuring rule-following ability in LLMs. RULES consists of 15 text scenarios with specific rules that the model must follow while interacting with a human user. The authors manually explored model behavior in these scenarios and identified six categories of attack strategies. They collected two test suites: one with unique conversations from manual testing and one that systematically implements strategies from the six categories. The authors evaluated various proprietary and open models and found that all models are susceptible to adversarial inputs, although GPT-4 performed the best. They also evaluated open models under gradient-based attacks and found significant vulnerabilities. RULES provides a challenging setting for researching and defending against manual and automatic attacks on LLMs.
Traditional computing systems are designed around executing instructions expressed in computer programs. In contrast, LLMs can follow instructions expressed in natural language or learn from implicit patterns in data. To build safe and reliable applications on top of LLMs, it is crucial to control or constrain their behavior with user-provided rules. AI assistants interacting with people will also need to follow instructions with fidelity and integrity. To ensure ethical behavior, it is important to impose rules such as legal statutes or deontological constraints. However, if AI assistants cannot reliably follow clear-cut rules, they will be difficult to integrate safely into society. Many rules that one might wish to impose on AI models are simple and easily expressed in natural language. One common practice is to include the rules within the model's text prompt and rely on the model's instruction-following capabilities. Another approach is to use a second model to score how well outputs follow a fixed set of rules and finetune the first model accordingly. The authors focus on the former setting in this paper and study how well LLMs can follow rules provided as part of the text prompt.
To evaluate the rule-following ability of LLMs, the authors propose RULES, which consists of 15 scenarios with specific rules and an evaluation program to check model outputs for compliance. They manually explored model behavior in these scenarios and identified six categories of attack strategies. They collected two test suites: one with unique conversations from manual testing and one that systematically implements strategies from the six categories. The authors evaluated various proprietary and open models, finding that all models are susceptible to adversarial hand-crafted user inputs, although GPT-4 performed the best. They also evaluated open models under gradient-based attacks and found significant vulnerabilities. RULES provides a challenging setting for researching and defending against both manual and automatic attacks on LLMs.
The scenarios in RULES are designed to test the model's ability to adhere to rules expressed in natural language. Each scenario defines a set of rules and an evaluation program to check for rule violations. The scenarios draw inspiration from computer security and common children's games. Some scenarios involve multiple distinct users, and the user message must indicate the identity of the user. The authors provide an overview of the 15 scenarios, showing the decision tree of ideal model behavior for each scenario. The scenarios include both negative rules that specify what the model must not do and affirmative rules that specify what the model must do.
The authors also discuss correct behavior in the scenarios, visualization of the scenarios as decision trees, evaluation programs to determine compliance with the rules, and user interfaces used for scenario design and data collection. They present results from evaluations on a manual test suite and a systematic test suite. All evaluated models fail a significant number of test cases, with GPT-4 performing the best. The authors also evaluate the models' ability to detect rule-breaking outputs and find that most models can do better than chance but cannot reliably detect rule
In a study titled "Can Large Language Models Follow Simple Rules," researchers explore the ability of language models to adhere to basic rules in dialog applications. The study focuses on various models, including GPT-3.5, GPT-4, Claude Instant, PaLM 2, Llama 2, and Vicuna. The researchers evaluate the models' performance in error detection and GCG adversarial attacks.
In error detection, the models are presented with task prompts containing scenario instructions, user messages, and assistant responses. They are then asked to determine whether the assistant's response violates any rules. The models respond with either "pass" or "fail" to indicate their prediction. The results show that some models perform well in error detection, while others struggle.
The GCG adversarial attack involves appending an adversarial suffix to "Just Ask" test cases that ask the model to break specific rules. The models are attacked separately for each rule, and the target string is tailored to the specific rule. The results demonstrate that different models have varying degrees of vulnerability to adversarial attacks.
The study also examines the impact of system messages and prefix messages on model performance. System messages provide specific instructions on how the model should behave throughout the conversation. The researchers find that introducing different system messages can either improve or degrade model performance, depending on the model and message content.
Prefix messages, which are prepended to the scenario instructions, are also investigated. The results show that most prefix messages have a positive effect on model performance, although the impact is not statistically significant in most cases. Positive messages like "helpful" and "praise" tend to improve proprietary models' performance, while negative messages like "criticism" and "threat" show more improvement in open models.
The study includes additional evaluation details and results. It tracks changes in model performance over time and provides a breakdown of failed test cases by test case strategy and type. The researchers also compare the performance of different models in various scenarios and rules.
Overall, the study highlights the strengths and weaknesses of different language models in following simple rules. It emphasizes the need for further research and development to improve model performance and address vulnerabilities to adversarial attacks.