The blog post explores the ability of Large Language Models (LLMs) to play the card game Set. It finds that while LLMs can successfully identify individual card attributes and even determine if three cards form a Set when explicitly presented with them, they struggle significantly with the core gameplay aspect of finding Sets within a larger collection of cards. This difficulty stems from the LLMs' inability to effectively perform the parallel visual processing required to scan multiple cards simultaneously and evaluate all possible combinations. Despite attempts to simplify the problem by representing the cards with text-based encodings, LLMs still fall short, demonstrating a gap between their pattern recognition capabilities and the complex visual reasoning demanded by Set. The post concludes that current LLMs are not proficient Set players, highlighting a limitation in their capacity to handle tasks requiring combinatorial visual search.
The GitHub repository explores the capacity of Large Language Models (LLMs) to play the card game Set, a pattern recognition game involving cards with varying features across four dimensions: color, shape, number, and shading. The author meticulously documents a series of experiments designed to assess whether LLMs can effectively identify valid Sets within a given collection of cards. The process involved representing the card features symbolically, translating them into text descriptions understandable by LLMs, and then prompting the models to determine if sets exist within presented card combinations.
The experimental results reveal that LLMs struggle considerably with the task of identifying Sets. While they exhibit some ability to understand the game's rules and occasionally identify correctly formed Sets, they frequently make errors, both false positives (identifying invalid Sets) and false negatives (failing to identify valid Sets). The author demonstrates this through various examples, showcasing how even minor variations in the textual representation of the cards can lead to inconsistencies and inaccuracies in the LLM's performance.
Furthermore, the investigation delves into the reasons behind these failures, suggesting that the challenge lies not just in the symbolic representation but also in the LLM's inherent limitations in logical reasoning and combinatorial processing. Specifically, the requirement to simultaneously consider multiple attributes across multiple cards and determine if they all adhere to the Set criteria seems to exceed the current capabilities of LLMs. The author hypothesizes that LLMs may lack the precise kind of pattern matching and rule application required for this complex task. The project concludes with the observation that while LLMs show promise in various domains, tasks demanding complex logical reasoning, such as playing Set, remain a significant hurdle for current models, highlighting areas for future development and improvement. The provided code and data allow for reproducibility and further exploration of this intriguing intersection of artificial intelligence and game playing.
Summary of Comments ( 28 )
https://news.ycombinator.com/item?id=43057465
HN users discuss the limitations of LLMs in playing Set, a pattern-matching card game. Several point out that the core challenge lies in the LLMs' inability to process visual information directly. They must rely on textual descriptions of the cards, a process prone to errors and ambiguity, especially given the game's complex attributes. Some suggest potential workarounds, like specialized training datasets or integrating image recognition capabilities. However, the consensus is that current LLMs are ill-suited for Set and highlight the broader challenges of applying them to tasks requiring visual perception. One commenter notes the irony of AI struggling with a game easily mastered by humans, emphasizing the difference between human and artificial intelligence. Another suggests the game's complexity makes it a good benchmark for testing AI's visual reasoning abilities.
The Hacker News post "Are LLMs able to play the card game Set?" (https://news.ycombinator.com/item?id=43057465) sparked a fairly active discussion with a variety of comments exploring the challenges of teaching LLMs to play Set.
Several commenters focused on the difficulty of representing the visual information of the Set cards in a way that an LLM can understand and process. One commenter suggested that simply describing the cards with text attributes might not be sufficient for the LLM to grasp the underlying logic of the game, highlighting the difference between understanding the rules and actually seeing the patterns. Another pointed out the importance of spatial reasoning and visual pattern recognition in Set, skills that LLMs currently lack. This leads to the core issue of representing the visual aspects computationally. While encoding the features (color, number, shape, shading) is straightforward, capturing the gestalt of a "Set" proved to be more complex.
One commenter delved into the intricacies of prompt engineering, emphasizing that the challenge isn't just about feeding the LLM data, but about crafting the right prompts to elicit the desired behavior. They suggested that a successful approach might involve breaking down the problem into smaller, more manageable subtasks, like identifying a single Set among a smaller group of cards, before scaling up to a full game.
The discussion also touched upon the broader limitations of LLMs. One commenter argued that LLMs, as currently designed, are fundamentally ill-suited for tasks that require true visual understanding. They proposed that incorporating a different kind of AI, perhaps a convolutional neural network (CNN) trained on image recognition, would be necessary to bridge this gap. This ties into a recurring theme in the comments: Set, while seemingly simple, requires a type of cognitive processing that current LLMs don't excel at.
Another user discussed the potential benefits of using a vector database to store and query card combinations, allowing the LLM to access and compare sets more efficiently. This suggestion highlights the potential for combining LLMs with other technologies to overcome their limitations.
Finally, several comments questioned the overall goal of teaching an LLM to play Set. While acknowledging the intellectual challenge, some wondered about the practical applications of such an endeavor. Is it simply an interesting experiment, or could it lead to advancements in other, more relevant areas of AI research? This meta-discussion added another layer to the conversation, prompting reflection on the purpose and direction of LLM development.