Evaluating Instruction-Following Capabilities of Large Language Models through Verbalizer Manipulation

Core Concepts

Instruction-following capability is an important aspect of large language models, but existing benchmarks primarily focus on common instructions that align well with model priors. This paper proposes a novel evaluation protocol called verbalizer manipulation to systematically assess models' ability to follow instructions that may not align with their prior knowledge.

Abstract

The paper proposes a novel instruction-following evaluation protocol called verbalizer manipulation. It involves constructing instructions that align with model priors to different extents - from natural (e.g., outputting "positive" for positive sentiment), to neutral (e.g., outputting "foo" for positive sentiment), to unnatural (e.g., outputting "negative" for positive sentiment). The authors evaluate four major model families (Flan-T5, GPT-Series, Vicuna, OPT-IML) across nine datasets and twelve sets of verbalizers. They find that: Larger models generally perform better on both natural and neutral instructions, indicating that scaling is an effective way to improve instruction-following ability. However, model performance diverges significantly on unnatural instructions, with no clear and consistent trend across model families. Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer. Examining verbalizers one by one, the authors find that models are not sensitive to verbalizers in natural instructions, but their performance diverges significantly in unnatural instructions, depending on the model family and verbalizer. Adding zero-shot chain-of-thought prompting can improve model performance on unnatural instructions, but large performance gaps still exist compared to natural instructions, especially for weaker model families. The results highlight the need for continued advancements to improve the instruction-following abilities of large language models, as they still have fundamental limitations in following instructions that contradict their prior knowledge.

Stats

"Larger models generally perform better on both natural and neutral instructions, indicating that scaling is an effective way to improve instruction-following ability." "Model performance diverges significantly on unnatural instructions, with no clear and consistent trend across model families." "Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer."

Quotes

"Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer, emphasizing the need for continued advancements to improve their instruction-following abilities." "When model scales to larger sizes, they still have difficulty in following instructions contradicting to prior knowledge even though they are allowed to output intermediate reasoning steps."

Key Insights Distilled From

Instruction-following Evaluation through Verbalizer Manipulation

by Shiyang Li,J... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2307.10558.pdf

Deeper Inquiries

How can we further improve the instruction-following capabilities of large language models beyond just scaling model size?

To enhance the instruction-following capabilities of large language models beyond just scaling model size, several strategies can be implemented: Diverse Training Data: Incorporating a more diverse range of training data that includes a wide variety of instructions and scenarios can help models generalize better to new tasks and instructions. Fine-tuning Techniques: Implementing more advanced fine-tuning techniques that focus specifically on instruction-following tasks can help models adapt better to new instructions and improve their ability to follow them accurately. Prompt Engineering: Developing more sophisticated prompt engineering techniques that guide models to focus on specific aspects of instructions can help improve their understanding and execution of tasks. Multi-Task Learning: Training models on a variety of tasks simultaneously can help them develop a broader understanding of language and instructions, leading to improved instruction-following capabilities. Human Feedback Integration: Incorporating human feedback loops during training can help models learn from their mistakes and improve their instruction-following abilities over time. Regularization Techniques: Implementing regularization techniques that encourage models to focus on instruction-following tasks and reduce overfitting to specific instructions can enhance their generalization capabilities.

What are the potential implications of models' limitations in following instructions that contradict their prior knowledge for real-world applications?

The limitations of models in following instructions that contradict their prior knowledge can have significant implications for real-world applications: Misinterpretation of Instructions: Models may misinterpret or incorrectly execute instructions that deviate from their pre-existing knowledge, leading to inaccurate or inappropriate responses in real-world scenarios. Lack of Adaptability: Models may struggle to adapt to new or unexpected instructions, limiting their flexibility and ability to perform tasks outside their training data. Risk of Errors: In situations where precise instruction-following is crucial, such as in healthcare, finance, or legal domains, models' limitations can result in errors that have serious consequences. Trust and Reliability Issues: Users may lose trust in models that consistently fail to follow instructions that challenge their prior knowledge, impacting their reliability and usability in practical applications. Ethical Concerns: Incorrect interpretation of instructions can lead to ethical dilemmas, bias propagation, or discriminatory outcomes, raising concerns about the ethical use of AI models in sensitive contexts.

How can we leverage the insights from this work to develop more robust and generalizable instruction-following models?

To develop more robust and generalizable instruction-following models based on the insights from this work, the following strategies can be implemented: Advanced Prompt Design: Designing more sophisticated prompts that guide models to focus on specific aspects of instructions and encourage accurate interpretation and execution of tasks. Verbalizer Manipulation Techniques: Further exploring and refining verbalizer manipulation techniques to evaluate models' ability to follow instructions that challenge their prior knowledge and incorporating these insights into model training. Task-Specific Training: Tailoring model training to specific instruction-following tasks and incorporating a diverse range of instructions to improve models' adaptability and generalization capabilities. Human-in-the-Loop Approaches: Integrating human-in-the-loop approaches to provide feedback and guidance to models during training, enabling them to learn from human demonstrations and improve their instruction-following abilities. Cross-Validation and Testing: Implementing rigorous cross-validation and testing procedures to evaluate models' performance on a wide range of instructions and tasks, ensuring their robustness and generalizability in real-world applications.