Why AI Struggles with Cryptic Crosswords

Cryptic crosswords are a special kind of puzzle where the clues are playful, tricky, and often full of wordplay. Solving them is not just about knowing words; you also need to think creatively and have a good grasp of the language. Despite the advancements in artificial intelligence, especially with Language Models, these puzzles still puzzle the machines. So, why are cryptic crosswords such a tough nut to crack for AI?

#What Are Cryptic Crosswords?

Cryptic crosswords are not your average crossword puzzles. In a standard crossword, clues usually mean exactly what they say, and the answers are straightforward synonyms. But in cryptic crosswords, the clues are a mix of riddles, anagrams, hidden words, and other wordplay tricks that disguise the actual answers. Think of it like a game of verbal hide and seek where you need to be both clever and knowledgeable.

For example, a cryptic clue might say, "Language model that's mixed up a llama." Here, the clue suggests an answer that has five letters. The 'definition' is "language model," and the 'wordplay' requires rearranging letters from "Alma," leading us to "LLaMA."

#Why AI Struggles with Cryptic Crosswords

Despite all the tech marvels we have today, AI still struggles to solve cryptic crosswords. Previous tests on various AI models, including Large Language Models (LLMs), have shown that they perform poorly compared to human solvers. In a study, some LLMs scored as low as 7% accuracy while expert human puzzlers nailed it with nearly 99% accuracy. That’s a big gap!

So, what’s going on? Here are a few reasons AI finds these puzzles challenging:

#1. Language Play is not So Straightforward

Cryptic clues often require thinking outside the box. A clue might ask for a synonym that doesn’t just match the meaning but also plays with the sounds or letters of words. AI models are trained to recognize and generate language based on patterns, but they often miss the subtle tricks in cryptic clues.

#2. Understanding Context Matters

To crack a cryptic clue, you need context. It’s not just about the words in the clue; it’s about the overall structure and how certain words signal particular types of wordplay. AI models may recognize terms but can miss their contextual importance, leading to wrong guesses.

#3. It’s All About Breaking It Down

To solve these puzzles, one effective approach is to break down clues into smaller parts: identifying the definition and figuring out the type of wordplay used. AI often struggles to do this effectively and may end up treating the entire clue as one indistinguishable block of text.

#The Quest for Answers

Researchers have been testing various AI models to see how well they perform on these tricky puzzles. They found that, while some models performed slightly better when prompted with specific instructions or given hints, they still lagged far behind human solvers. For example, giving the AI the definition part of a clue improved its performance, but it still couldn’t match human expertise.

#The AI Testing Grounds

Different models have been tested on cryptic crosswords, including some popular ones like ChatGPT, Gemma2, and LLaMA3. These models were pitted against datasets containing a large number of cryptic clues to see how they fared under different conditions. While some models showed better results than others, none came close to achieving human-like accuracy.

#A Peek into AI’s Puzzle-Solving Process

Researchers didn’t just stop at testing how well AI could solve these clues. They also looked into how these models thought – or rather, how they attempted to think. Specifically, they focused on three areas:

1. Extracting Definitions: Could the model pull out the definition part of a clue? Surprisingly, they did better at this than at solving the entire puzzle, likely because this task often just involved recognizing words.

2. Identifying Wordplay: This is where things got tricky. Researchers tested whether the models could determine the type of wordplay used in different clues. While some models could pick up on certain indicators, they often missed the mark.

3. Explaining the Solution: The final test involved asking the models to explain how they arrived at their answers. Their explanations often lacked clarity, showing that they did not fully grasp the processes involved in solving the clues.

#Results and Observations

After these tests, it became clear that although AI has made strides in language processing, solving cryptic crosswords remains a significant challenge. While ChatGPT performed the best among the models tested, it still couldn’t match the accuracy of dedicated human solvers. Funny enough, it seems that the combination of wit and practice that humans possess is something that AI is still trying to catch up to.

#The Definition Extraction Task

When tasked with extracting the definition from clues, AI performed relatively well, as they could directly pull from the words in the clue. But determining the underlying wordplay was a different story. For instance, professional human solvers often look for key indicator words that hint at the type of wordplay being used. The models didn’t always pick up on these subtle signals.

#Wordplay Type Detection

Researchers identified five main types of wordplay commonly found in cryptic clues: anagram, assemblage, container, hidden word, and double definition. AI struggled significantly with this, often misclassifying clues. For example, one model might frequently predict "anagram," while another might lean towards "hidden word." This inconsistency indicates a lack of a solid grasp of wordplay types on the part of the AI.

#Explanation and Reasoning

When asked to explain their reasoning, the models displayed varying degrees of understanding. Some broke down the clues into parts but often combined unrelated elements, leading to confusing outputs. ChatGPT sometimes hinted at operations like anagramming or assembling words but struggled to provide accurate explanations.

#The Road Ahead for AI in Crossword Solving

Despite the hurdles, there’s hope for the future. The researchers believe that by exploring advanced techniques such as chain-of-thought reasoning – breaking tasks into smaller, manageable subtasks – AI’s performance could improve. Similarly, incorporating curriculum learning, where models gradually engage with more complex tasks, might enhance their abilities.

#Future Research Directions

1. Chain of Thought Models: These methods could teach AI to solve problems step-by-step rather than attempting to tackle the whole puzzle at once.

2. Curriculum Learning: Starting with simpler puzzles before moving to more complex ones could help AI build the skills it needs to solve cryptic crosswords.

3. Specialized Models: Using a mixture of expert models that are trained on different wordplay types might lead to more precise solutions.

#Limitations of the Current Study

Researchers noted a few limitations in their work. They only tested a small selection of language models, which means results might not reflect the capabilities of other AIS. Additionally, the datasets used were not vast in number and may not provide a complete picture of the models’ abilities.

#Real-World Scenarios

In reality, human solvers don’t just tackle one clue at a time; they often work on solving multiple clues in a grid. Each answer can provide hints for others, making the solving process interactive and dynamic. In contrast, researchers focused on individual clues to investigate how AI interprets them, which may not fully represent real-world solving strategies.

#Data Contamination Concerns

Interestingly, ChatGPT outperformed the others, but researchers could not assess its training setup or if it used any crossword data during training. Though there’s a possibility of "contamination," it appears that all models still find cryptic clues challenging, indicating they cannot merely memorize answers from past experiences.

#Conclusion

The study sheds light on the current state of AI capabilities in solving cryptic crosswords. Although AI systems have advanced significantly in language processing, cracking these puzzles is still a major challenge. While improvements can be made, there’s a long way to go before AI can match the skill and cunning of human solvers. For now, it seems that when it comes to cryptic crosswords, humans still reign supreme – at least until AI gets a sense of humor and some wordplay practice!

In the world of puzzles, it looks like AI is still solving the mystery of the cryptic crossword. Keep those pencils ready; humans are still ahead in this playful battle of wits!