Breaking Down Complex Tasks for Better Results

Every day, people perform many tasks, ranging from simple ones to complex ones. Sometimes, breaking down these tasks into smaller parts can help complete them more efficiently. For example, planning a wedding involves numerous Steps and takes a lot of time, while washing a cup may only take a few seconds.

When people have a clear plan with Actionable steps, they tend to finish tasks faster and with better results. Research shows that splitting a large task into smaller parts can improve accuracy and help recover from interruptions. It can also assist in organizing information or collaborating on projects.

In this work, we focus on how to break down complex tasks into smaller, manageable steps, along with figuring out the order in which to do them. This process is called Structured Complex Task Decomposition (SCTD) and involves creating a visual representation, called a Task Graph. In this graph, each step is a node, and connections between them show which steps depend on others.

#The Importance of Task Decomposition

Understanding how to break down tasks is crucial for creating helpful planning tools. It also presents a challenge for machines, as they need to share knowledge about how tasks work in the real world. We explore how well large language models (LLMs) can break down tasks effectively using their knowledge.

We have created a dataset with examples of complex tasks and how they can be decomposed. This dataset allows us to test machine approaches against others that don't use LLMs. Our findings show that LLMs can break down tasks into steps quite effectively. However, they struggle with understanding how different steps relate in terms of timing.

In our daily lives, we deal with tasks that vary in complexity and time needed to complete them. Tasks like washing dishes are quick, while others like organizing a wedding can take weeks. By breaking down big tasks into smaller steps, we can execute them more easily. Studies suggest that when tasks are laid out in clear steps, success rates improve.

#Task Graphs

A Task Graph represents a complex task with its various steps and their relationships. For instance, when making mayonnaise, there are several steps to follow. The goal is to create a complete set of steps and specify which steps must be completed first.

Over the years, there has been much research on how to tackle task decomposition using artificial intelligence. Because this problem requires a lot of reasoning, it is a significant challenge in the AI field. Many existing solutions rely on crowd-sourcing or analyzing search queries from the internet. However, we aim to see how well LLMs can provide this information directly.

Our dataset includes human-annotated tasks where annotators provide context and steps for each task. We also gather information about the order of operations between these steps. This enables us to compare how well LLMs perform on this task compared to other methods.

We must address the issue of measuring quality in the steps we generate. In previous work, adding duplicate steps could artificially improve performance metrics. To tackle this, we propose more reliable metrics and assess how well LLMs perform against other methods.

#Human Annotation of Tasks

In creating our dataset, we collected tasks from various sources. One source includes logs from task management applications, while another comes from popular search engine queries. We focused on tasks that require multiple steps to complete while ensuring we excluded sensitive topics.

Our annotators followed specific guidelines to write down all required steps after noting down their assumptions. This way, we ensure that all steps are meaningful and actionable. Throughout the data-gathering process, we had our annotators go through several training rounds to improve their quality of work.

Eventually, we gathered thousands of steps across many tasks, which represent the fundamental actions people need to take to complete their goals. We also had a separate set of annotators map the Dependencies between these steps to see which must happen first.

#Generating Steps

To generate the steps for a task, we tried various strategies. One straightforward approach involves showing the model a few examples of tasks and asking it to generate steps for a new task based on those examples. This is known as In-Context Learning (ICL).

Since different attempts to generate steps can yield diverse outcomes, we also experimented with generating multiple sequences and filtering them to find the best steps. This approach indicated that using different models could produce complementary information, leading to more comprehensive step collection.

We also implemented techniques where the LLM is tuned to learn from specific training data. Learning the right kind of prompts and responses helps the model produce more accurate outputs.

#Measuring Quality of Steps and Dependencies

To evaluate the quality of the steps we generated, we focused on two main areas: how well the steps align with the expected actions and how accurately the dependencies between steps are captured.

For measuring step quality, we adopted a matching approach that assesses how steps produced by the model align with the golden steps created by humans. This helps determine whether the model can produce meaningful and relevant steps for the given task.

When measuring temporal dependencies, we look at how well the model predicts the order of steps. It’s important to recognize that while LLMs can generate steps effectively, they often struggle to identify the correct order of those steps.

#Results of Task Decomposition

We ran various tests to compare the performance of LLMs against other models. Our results indicate that language models outperform traditional methods by a significant margin in generating step sequences.

For instance, even the simplest LLM approach showed notable improvements in generating accurate steps compared to methods that rely solely on frequency or similarity-based approaches. Further advancements were achieved by combining multiple strategies or fine-tuning the models.

Nonetheless, it was evident that while these models excel at generating the right sequence of steps, they still have gaps when it comes to establishing the relationships between those steps.

#Context Understanding

The context in which tasks are performed can significantly affect the steps required to complete them. For example, the process to recover deleted photos can differ based on the device being used.

In our dataset, context plays a crucial role. We demonstrated that providing context greatly improved the performance of the models. LLMs can adapt the generated steps based on the situations described. This reflects their ability to adjust to the given context and provide relevant details for each task.

#Addressing Temporal Dependencies

Examining how well LLMs can predict the order of steps is a vital part of our work. Initial findings show that LLMs struggle with this task, especially when determining precise relationships between steps.

When using specific techniques like soft-prompt tuning, models showed improved performance in understanding these dependencies. However, the overall ability of LLMs to judge whether one step should come before another still requires attention and further refinement.

#Quality Issues in Existing Datasets

In comparing our dataset with existing ones, we found several quality issues that prompted us to develop our probe. Some common problems include overlap between training and test sets, where similar tasks appear in both. This can lead to inaccuracies in measuring model performance.

Other concerns included irrelevant steps, parsing issues resulting in unclear instructions, and cases where advice instead of actionable steps was provided. These quality problems highlight the need for carefully curated datasets that reflect high standards.

#Conclusion

Overall, our exploration into structured complex task decomposition using language models has yielded promising results. We established that while LLMs can effectively generate step sequences, they need further improvement in grasping the temporal relationships between those steps.

Future research could focus on enhancing the models' understanding of task dependencies or exploring new ways to generate task graphs. By addressing these areas, we can advance the capabilities of LLMs in handling complex real-world tasks. This work opens the door for more sophisticated tools that assist users in organizing their tasks and achieving their goals more effectively.