arXiv:2403.12094v1 Announce Type: new
Abstract: Cryptic crosswords are puzzles that rely not only on general knowledge but also on the solver’s ability to manipulate language on different levels and deal with various types of wordplay. Previous research suggests that solving such puzzles is a challenge even for modern NLP models. However, the abilities of large language models (LLMs) have not yet been tested on this task. In this paper, we establish the benchmark results for three popular LLMs — LLaMA2, Mistral, and ChatGPT — showing that their performance on this task is still far from that of humans.
Analyzing the Challenges of Cryptic Crosswords for NLP Models
Cryptic crosswords present a unique challenge for natural language processing (NLP) models, as they require not only an understanding of general knowledge but also the ability to manipulate language at various levels and tackle different types of wordplay. While NLP models have made significant progress in various language tasks, their performance on cryptic crosswords remains a hurdle that has yet to be overcome.
Previous research has already highlighted the difficulty of solving cryptic crosswords for modern NLP models. However, this study aims to shed further light on their performance by testing three popular large language models (LLMs) – LLaMA2, Mistral, and ChatGPT – on this particular task.
The results of the benchmark test conducted in this study demonstrate that the performance of the LLMs on cryptic crosswords is still considerably below that of humans. This further confirms the complexity and multi-disciplinary nature of the task, which requires a deep understanding of both language and domain-specific knowledge.
The Role of General Knowledge
One crucial aspect of solving cryptic crosswords is the ability to draw upon a wide range of general knowledge. These puzzles often incorporate clues that refer to historical events, cultural references, scientific facts, and more. Thus, NLP models must possess a vast knowledge base to have a chance at deciphering the cryptic hints and arriving at the correct solutions.
While large language models do excel at capturing vast amounts of information, their performance in the realm of general knowledge still falls short compared to human abilities. This highlights the need for further research and improvement in this aspect to enhance the overall performance of NLP models in solving cryptic crosswords.
Wordplay and Language Manipulation Challenges
Cryptic crosswords also heavily rely on wordplay and manipulation of language. Clues often involve anagrams, hidden words, puns, double meanings, and other linguistic tricks. Understanding and deciphering these wordplay elements require a deep grasp of language nuances and creative thinking skills.
NLP models typically excel at understanding straightforward language usage but struggle with complex language manipulations and wordplay. The lack of contextual understanding in some cases hinders their ability to identify the intended wordplay and reach the correct solution. Advancements in NLP models need to focus on capturing the intricacies of language manipulation better to improve their performance in this specialized domain.
The Interdisciplinary Nature of Solving Cryptic Crosswords
One intriguing aspect of cryptic crosswords is the integration of multiple disciplines. To successfully solve these puzzles, one often needs to draw on knowledge from various fields, such as history, science, literature, and popular culture, just to name a few. This multidisciplinary nature adds an extra layer of complexity to the task.
The involvement of diverse domains makes it challenging for NLP models to possess the breadth of knowledge required for accurate clue interpretation and solution finding. Developing more interdisciplinary approaches that incorporate multiple knowledge sources, as well as advanced reasoning abilities, could potentially enhance the performance of NLP models in cryptic crossword solving.
Conclusion
The benchmark results presented in this study emphasize the ongoing challenge faced by NLP models when it comes to solving cryptic crosswords. The complexity of these puzzles, which require a combination of general knowledge, language manipulation skills, and multidisciplinary understanding, presents obstacles that current models are unable to overcome.
Further research and advancements in NLP techniques need to address the limitations highlighted in this study. Enhancing models’ general knowledge, capturing intricate language manipulations, and integrating interdisciplinary approaches are key areas for improvement. As NLP models continue to evolve, it is possible that future iterations will achieve human-level performance in deciphering the intricacies of cryptic crosswords.