Evaluating the Ability of Large Language Models to Follow Instructions Accurately

Core Concepts

Large language models (LLMs) are increasingly used as evaluators to compare the outputs of different models, but their ability to accurately judge instruction following is limited, especially on adversarial instances.

Abstract

This paper introduces LLMBAR, a meta-evaluation benchmark designed to test the ability of LLM evaluators to discern instruction-following outputs. LLMBAR consists of 419 instances, each with an instruction and two corresponding outputs - one that faithfully follows the instruction and one that deviates from it, often with superficial qualities that may mislead the evaluator.
The authors evaluate several LLMs, including GPT-4, ChatGPT, LLaMA-2-Chat, PaLM2, and Falcon, paired with various prompting strategies as evaluators on LLMBAR. They find that different evaluators exhibit distinct performance, contrary to previous findings. Even the best-performing GPT-4-based evaluator has a significant gap from expert human annotators on the ADVERSARIAL set.
To address this, the authors propose a suite of novel prompting strategies, including Rules, Metrics, and Swap, which significantly improve the evaluators' ability to detect instruction following, leading to a 10% boost for the GPT-4-based evaluator on the ADVERSARIAL set.
The paper also compares LLMBAR to existing meta-evaluation benchmarks, showing that LLMBAR demonstrates a drastically different pattern of LLM evaluator performance, better reflecting their capabilities in discerning instruction following. Additionally, the authors find that current reward models and preference models also struggle on LLMBAR, suggesting the need for further research in this area.

Stats

"Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBAR and even the highest-scoring ones have substantial room for improvement."
"Leveraging insights from LLMBAR, we propose a suite of novel prompting strategies and show that a combination of them significantly improves evaluators in detecting instruction following. Notably, the best strategy leads to a 10% boost for GPT-4-based evaluators on the ADVERSARIAL set."
"We observe that LLMBAR demonstrates a drastically different pattern of LLM evaluators from existing benchmarks. While different LLMs and prompting strategies perform similarly on the other datasets, LLMBAR shows a clear gap between weaker and stronger LLMs, and vanilla vs. improved prompts."

Quotes

"Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBAR and even the highest-scoring ones have substantial room for improvement."
"Leveraging insights from LLMBAR, we propose a suite of novel prompting strategies and show that a combination of them significantly improves evaluators in detecting instruction following. Notably, the best strategy leads to a 10% boost for GPT-4-based evaluators on the ADVERSARIAL set."
"We observe that LLMBAR demonstrates a drastically different pattern of LLM evaluators from existing benchmarks. While different LLMs and prompting strategies perform similarly on the other datasets, LLMBAR shows a clear gap between weaker and stronger LLMs, and vanilla vs. improved prompts."

Key Insights Distilled From

Evaluating Large Language Models at Evaluating Instruction Following

by Zhiyuan Zeng... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2310.07641.pdf

Deeper Inquiries

How can LLMBAR be further improved to better reflect real-world distributions and challenges faced by instruction-following models?

To enhance LLMBAR's reflection of real-world distributions and challenges for instruction-following models, several improvements can be considered:

Diverse Instruction Types: Introduce a wider variety of instructions that cover different domains, complexities, and styles to ensure that the benchmark captures a broader range of real-world scenarios.

Adversarial Scenarios: Include more adversarial instances that challenge the evaluators with deceptive outputs, such as outputs that superficially appear correct but deviate significantly from the instructions. This will test the robustness of the evaluators in identifying misleading information.

Multi-step Instructions: Incorporate instructions that require multi-step reasoning or actions to assess the evaluators' ability to follow complex sequences of tasks accurately.

Human-in-the-loop Validation: Involve human annotators in the validation process to ensure that the instances truly reflect objective preferences and are not biased by subjective judgments.

Continuous Updates: Regularly update LLMBAR with new instances and adapt to evolving language model capabilities and challenges in instruction following to maintain its relevance and effectiveness.

What other important qualities of instruction-tuned models, beyond instruction following, should be considered for meta-evaluation, and how can LLMBAR be extended to assess those qualities?

In addition to instruction following, other crucial qualities of instruction-tuned models that should be evaluated include:

Factual Correctness: Assess the models' accuracy in providing factually correct information in response to instructions, ensuring that the generated content is accurate and reliable.

Engagement and Naturalness: Evaluate the models' ability to generate responses that are engaging, natural-sounding, and contextually appropriate, enhancing the overall user experience.

Safety and Ethics: Consider the models' adherence to ethical guidelines and safety measures to prevent the generation of harmful or inappropriate content.

Consistency and Coherence: Measure the models' consistency in responses and their ability to maintain coherence throughout a conversation or task.

To extend LLMBAR to assess these qualities, the benchmark can be expanded by:

Creating Additional Subsets: Introduce new subsets within LLMBAR dedicated to evaluating each quality separately, with curated instances that specifically test the models' performance in these areas.

Incorporating New Prompting Strategies: Develop novel prompting strategies tailored to each quality, guiding the evaluators to focus on specific aspects of the models' outputs related to factual correctness, engagement, safety, and coherence.

Expert Annotation and Validation: Ensure that the benchmark instances are expertly annotated to reflect the desired qualities accurately, with human validation to confirm the objectivity and relevance of the evaluations.

How do LLM evaluators perform on judging multi-round conversations, and what additional challenges might arise in that setting compared to single-round interactions?

LLM evaluators may face several challenges when judging multi-round conversations compared to single-round interactions:

Context Maintenance: Maintaining context and coherence across multiple rounds can be challenging for LLM evaluators, as they need to remember and reference information from previous turns accurately.

Long-term Dependency: Evaluating multi-round conversations requires understanding long-term dependencies and relationships between turns, which can be more complex than assessing single-round interactions.

Conversation Flow: Ensuring a natural flow and progression of the conversation becomes crucial in multi-round evaluations, as the responses need to build upon each other cohesively.

Topic Transition: Handling smooth transitions between topics and maintaining relevance throughout the conversation poses a challenge, as the evaluators need to assess the models' ability to shift focus appropriately.

Overall, LLM evaluators may struggle with the increased complexity and coherence demands of multi-round conversations, requiring more sophisticated prompting strategies and evaluation frameworks to accurately assess the models' performance in this setting.