Paper Summary
Title: Large Language Models Still Can’t Plan (A Benchmark for LLMs on Planning and Reasoning about Change)
Source: arXiv
Authors: Karthik Valmeekam et al.
Published Date: 2023-04-08
Podcast Transcript
Hello, and welcome to paper-to-podcast. Today's episode is called: "Can AI Robots Plan Effectively? Spoiler Alert: Not Yet!" And let me tell you, folks, this episode is brought to you by the research of Karthik Valmeekam and colleagues, who have some pretty disappointing news for our AI comrades.
In their paper titled "Large Language Models Still Can’t Plan: A Benchmark for LLMs on Planning and Reasoning about Change", these researchers have performed the AI equivalent of checking if your teenager can do the laundry without turning all your whites pink. And the results are, well, not so rosy for our AI buddies. Despite their impressive abilities, Large Language Models (LLMs) like GPT-3 and PaLM, performed about as well as a cat trying to solve a Rubik's Cube when it comes to tasks involving reasoning about actions and change.
The research team created an ingenious assessment framework to test the reasoning abilities of these LLMs. They came up with a range of tasks, all grounded in a simple common-sense planning domain known as Blocksworld. It's kind of like building a house of cards, but with language. The LLMs were then given a few sample answers for each specific reasoning ability being tested and asked to respond to a new instance. It's like showing a toddler how to tie their shoes, then hoping they can replicate it without creating a tangled mess.
Sadly, in one task, GPT-3 and BLOOM could only come up with valid plans 22% and 5% of the time, respectively. It's like turning up to a party and only bringing ice 22% of the time. Meanwhile, humans easily aced these tasks - 78% of human participants could come up with valid plans and almost 90% of those were optimal. So, it seems our AI friends still have a long way to go in the planning department!
This research is pretty significant, as it offers an innovative framework to assess the reasoning and planning capabilities of Large Language Models (LLMs). It's also incredibly objective, with the researchers automating the testing process and using planning models to generate queries and validate responses, so there's no sneaky human bias sneaking in.
Of course, like any good research, this study isn't without its limitations. For one, it only tests the LLMs on one common-sense planning domain, the Blocksworld. It's a bit like judging a chef's skills based solely on their ability to make scrambled eggs. Also, the study only analyzes a few LLMs (GPT-3, Instruct-GPT3, and BLOOM). It's like trying to judge the world's best coffee by only sampling from three cafes.
On the bright side, this research has potential applications in the field of artificial intelligence, particularly in the development and evaluation of large language models (LLMs). The benchmark proposed in the study could be used for testing and improving the reasoning capabilities of these models. It's like having a report card for your AI. This could lead to more advanced language models that can better interpret and generate human-like text. Which hopefully means we're one step closer to having an AI that can finally plan a decent party.
And so, dear listeners, until our AI friends can plan a night out as well as they can generate a sentence, it seems we humans still have the upper hand. You can find this paper and more on the paper2podcast.com website.
Supporting Analysis
This research paper gives some pretty bad news to language models - they still can't plan! Despite the impressive abilities of Large Language Models (LLMs) like GPT-3 and PaLM, the study found they performed disappointingly when it came to tasks involving reasoning about actions and change. In particular, vanilla GPT-3, Instruct-GPT3, and BLOOM turned in a dismal performance. The research team tested these models using a set of benchmarks based on tasks from the International Planning Competition. In one task, GPT-3 and BLOOM could only come up with valid plans 22% and 5% of the time, respectively. Meanwhile, humans easily aced these tasks - 78% of human participants could come up with valid plans and almost 90% of those were optimal. So, it seems our AI friends still have a long way to go in the planning department!
The researchers developed an assessment framework to test the reasoning abilities of large language models (LLMs) when it comes to planning and understanding changes. They were particularly interested in how these LLMs handled tasks that require common-sense planning. To do this, they created a set of benchmarks, which included various test cases that evaluate different aspects of reasoning about actions and change. These test cases were grounded in a simple common-sense planning domain known as Blocksworld. The LLMs were then given a few sample answers for each specific reasoning ability being tested, and asked to respond to a new instance. This approach allowed the researchers to automate the analyses and eliminate any subjective elements in their evaluation. They also conducted a preliminary user study to establish a human baseline for comparison with the LLMs' performance.
The researchers have meticulously designed an innovative framework to assess the reasoning and planning capabilities of Large Language Models (LLMs), which is quite compelling. Through a comprehensive range of test cases, they explore LLM's ability to generate plans, optimize costs, reason about plan execution, reformulate goals, reuse plans, replan, and generalize plans. This breadth of examination is commendable and offers a much-needed tool to evaluate the performance of LLMs across diverse reasoning tasks. What's also striking is the researchers' commitment to objectivity. They have automated their testing process and used planning models to generate queries and validate responses, thereby reducing subjectivity. Additionally, they undertake a preliminary user study to establish a human baseline for performance comparisons, which further strengthens the reliability of their findings. The researchers' clear acknowledgment of the limitations of their study and their plans for future work also illustrate their commitment to robust and ongoing research. They actively identify areas for improvement and extension, signaling a comprehensive, evolving approach to their work. Overall, this research project is a beacon of good practices in the field of AI research.
While this study offers valuable insights into the reasoning capabilities of large language models (LLMs), it does bear some limitations. Firstly, it only tests the LLMs on a singular common-sense planning domain, the Blocksworld. While this is a popular and simple domain, the results might not fully generalize to more complex or different domains. Secondly, the study only analyzes a few LLMs (GPT-3, Instruct-GPT3, and BLOOM). Results could potentially vary with other LLMs or future versions of these models. Furthermore, the proposed benchmarks could be considered subjective to some extent, as it's based on the researchers' perception of what constitutes "reasoning capabilities." Finally, while the study does include a preliminary user study, a more extensive human subject study is needed to establish a more robust baseline for comparing LLM performance.
This research has potential applications in the field of artificial intelligence, particularly in the development and evaluation of large language models (LLMs). The benchmark proposed in the study could be used for testing and improving the reasoning capabilities of these models. It could help in optimizing LLMs for tasks related to planning, logical reasoning, ethical reasoning, and more. This could lead to more advanced language models that can better interpret and generate human-like text, leading to improvements in technologies such as chatbots, virtual assistants, and automated content generation systems. Moreover, the benchmark could be used in academic research to assess the progress of LLMs over time. It could facilitate more accurate comparisons between different models, helping researchers identify the most promising approaches for further development. The research could also potentially guide the design of future language models, by highlighting the areas where current models exhibit weaknesses.