Evaluating Model Performance in Understanding Plan Dependencies

Understanding how to follow plans, like recipes or Instructions, is important for making decisions in systems. One key part of plans is the order in which steps should happen, which shows how they depend on each other.

We created a new tool called Step Order Prediction to check if one step needs to happen before or after another in cooking recipes. We used this to see how well Models can learn about these Dependencies. Our findings show that the best models currently do not perform well, suggesting that there is much to improve. When we ask for explanations along with answers, performance gets better, but there's still a long way to go.

#The Importance of Planning

Planning is vital for decision-making in various fields, such as robotics and environments where actions are performed by machines. To create, adjust, or follow a plan, it's necessary to understand the steps and their relationships.

Previous studies on reasoning in plans have mainly focused on simpler problems or controlled environments. However, real-life plans, often written in natural language, cannot be tested in the same way for accuracy and reliability. Our work aims to evaluate how well models can understand these connections in complex plans.

#Introducing the Benchmark

We developed a benchmark to assess how models understand causal and temporal relationships in plans. Using a dataset of cooking recipes, we created questions that require reasoning about different types of relationships among steps, such as what needs to happen before or after other actions.

For example, in the process of making a cake, it's important to recognize when certain ingredients need to be mixed. If almonds should be added before mixing, there's a reason: to ensure everything mixes evenly. If flour can be added at any time without affecting other steps, that shows different dependencies.

To create our benchmark, we used an existing recipe dataset and turned it into a set of questions about how steps relate to one another. This dataset contains thousands of questions about dependencies across several recipes.

#Evaluation of Models

In our study, we evaluate various models to see how well they respond to our benchmark. We found that while models can produce good outputs, their ability to truly understand the relationships in plans is lacking.

When assessing their performance, we look at how often their predictions match the necessary order of steps. Since many models show a tendency to predict steps as dependent, we need to analyze their reasoning further.

Using explanations helps improve performance, but even with this improvement, areas still need work. Human evaluators can help determine how well models explain their reasoning. We discovered that models often disagree with human judgments on their answers.

#Performance Insights

From our Evaluations, we see that models struggle to identify step dependencies accurately. Most predictions hover around random guesses, indicating that they have not grasped the intricacies of instructional texts.

While some models do somewhat better when asked for explanations, the overall performance remains inadequate. Human evaluations also reveal that model explanations often lack depth, leading to average scores that suggest they are not very convincing.

Interestingly, when we asked models to explain their answers after responding instead of using a chain-of-thought prompting (where they reason before answering), they performed better. This indicates flaws in their reasoning approach.

#The Framework for Analysis

To thoroughly analyze model performance, we look into specific metrics. We define consistency in predictions when asked similar questions about the same steps. Our findings indicate that even the best-performing models often change their answers when asked in different ways, showing instability.

For pairs of steps that can happen in any order, we create a special test. If a model treats two independent steps as dependent, that suggests it is using step order as a heuristic instead of truly understanding their relationships.

When we compare different prompting methods, we see that using explanations improves predictions. This prompts us to further investigate how well models handle dependency questions and if prompting strategies could enhance understanding.

#Exploring Types of Errors

Throughout our analysis, we identified various errors made by models. These fall into four main categories:

1. Multi-hop Dependency: Here, models fail to see how two steps might depend on each other through an intermediate step. For example, if baking relies on mixing ingredients first, missing this connection leads to errors.

2. Effects: Models sometimes don't recognize that the result of one step can enable the next. For instance, cooling a cake can only happen after it's baked.

3. Preconditions: This involves failing to realize what must be true for a step to occur. Adding sauce to meatballs can't happen if the meatballs haven't been cooked first.

4. Irrelevant Answers: Occasionally, models provide answers that don't relate to the asked question. This loss of focus shows a lack of understanding about the steps and their context.

These errors illustrate that models do not yet capture the complexity of planning and reasoning, and we highlight the need for further development.

#Conclusion

The ability to understand plans and their dependencies is crucial for intelligent systems. Our research reveals that current models struggle significantly to grasp these relationships in cooking recipes. We have created a benchmark that helps evaluate this performance, showing areas needing improvement.

While steps of explanation can enhance accuracy, models still exhibit biases and inconsistencies that hinder their understanding. Human evaluations show that the explanations provided are often insufficient, emphasizing the ongoing need for better reasoning capabilities.

In the future, we plan to investigate various domains beyond cooking recipes, such as medical guidelines, repair manuals, and software tutorials. This broader approach may lead to further insights into reasoning and understanding in complex environments.

Overall, the progress in model capabilities shows promise, but the results underscore the need for continued work on developing reliable systems capable of understanding the intricacies of planning.