Language Models are Crossword Solvers

Soumadeep Saha, Sutanoya Chakraborty, Saptarshi Saha, Utpal Garain·June 13, 2024

Summary

This paper investigates the capabilities of large language models (LLMs) in solving crossword puzzles, focusing on both American and cryptic styles. The authors find that current LLMs, like GPT-4 Turbo, significantly outperform previous models in cryptic puzzles, closing the gap with human experts by 2-3 times. They develop the SweepClip algorithm, which integrates LLMs into a full crossword-solving process, achieving 93% accuracy on New York Times puzzles. The study highlights LLMs' progress in natural language understanding, wordplay, and constraint satisfaction, but also identifies limitations like difficulty with character counting and the need for grid information. The research suggests that LLMs are becoming competitive with human solvers and could further improve with future advancements.

Key findings

11

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of solving crossword puzzles with the assistance of Language Models (LLMs) by proposing an algorithm called SweepClip . This problem involves not only generating correct answers from provided clues but also leveraging constraints from previously generated words and back-tracking to eliminate incorrect answers when new information becomes available . While traditional approaches to solving crosswords involve candidate answer proposal systems and grid-filling algorithms, the use of LLMs in this context presents a new high bar for AI systems . The paper explores the performance of LLMs in solving crossword puzzles, highlighting the potential for LLMs to excel in this task with future advancements .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that language models (LLMs) have the ability to count sub-tokens based on information provided during training, specifically focusing on the task of sub-token counting . The study aims to investigate whether LLMs can accurately count the number of characters in a word or phrase, and how their performance in this task varies based on the frequency of the token . The hypothesis is centered around understanding how LLMs handle sub-token counting and whether they can generalize this ability across words with different prevalence levels .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Language Models are Crossword Solvers" proposes several innovative ideas, methods, and models in the realm of solving crossword puzzles using Large Language Models (LLMs) . One key contribution is the demonstration of the significant competence of current-generation state-of-the-art (SoTA) language models in deciphering cryptic crossword clues, surpassing previous SoTA results by a factor of 2-3 in relevant benchmarks . The paper introduces a search algorithm that leverages the performance of LLMs to solve full crossword grids for the first time, achieving an impressive accuracy of 93% on New York Times crossword puzzles .

The paper delves into the analysis of LLMs' abilities to solve crossword puzzles, aiming to understand the strengths and weaknesses exhibited by SoTA LLMs . It highlights that solving crossword puzzles necessitates high proficiency in contextual clue understanding, wordplay, world knowledge, and reasoning . The research explores various skills involved in solving cryptic crossword clues, such as connecting clues to relevant knowledge, reasoning, and wordplay . Additionally, the paper emphasizes the importance of adherence to character and length constraints in crossword solving .

One novel aspect introduced in the paper is the development of a search algorithm, named SweepClip, which significantly enhances the performance of LLMs in solving crossword puzzles . This algorithm improves clue-wise answer accuracy and boosts the overall crossword solving accuracy by exploiting constraint information to a greater extent than traditional question-answering approaches . SweepClip implicitly conducts self-consistency checks to refine candidate answers, leading to improved performance in solving crosswords .

Furthermore, the paper presents results that showcase the substantial performance gains achieved by current-generation SoTA LLMs in deciphering cryptic crossword clues . By employing innovative prompting techniques like Chain-of-thought prompting with self-consistency, the paper demonstrates enhanced performance in solving cryptic crossword clues compared to previous SoTA results . This approach leads to a notable improvement in accuracy without the need for fine-tuning . The paper "Language Models are Crossword Solvers" introduces novel characteristics and advantages compared to previous methods in solving crossword puzzles using Large Language Models (LLMs) . Here are some key points:

  1. Significant Performance Improvements: The current generation of state-of-the-art (SoTA) LLMs exhibit remarkable proficiency in solving both straight and cryptic crossword puzzle clues, surpassing previous results by a substantial margin . These LLMs demonstrate enhanced aptitude in deciphering cryptic crossword clues, with GPT-4 Turbo achieving an accuracy of 18.70% on the cryptic crossword task, showcasing a significant improvement over previous SoTA results .

  2. Innovative Algorithm - SweepClip: The paper introduces an innovative search algorithm called SweepClip, which significantly enhances the performance of LLMs in solving crossword puzzles . This algorithm improves clue-wise answer accuracy and boosts overall crossword solving accuracy by exploiting constraint information more effectively than traditional question-answering approaches . SweepClip implicitly conducts self-consistency checks to refine candidate answers, leading to improved performance in solving crosswords .

  3. Generalizability and Reasoning Abilities: The research delves into the generalizability of LLMs beyond potential data contamination in their training set . The models demonstrate the capacity to reason about unseen cryptic crossword clues and arrive at correct responses, as evidenced by human evaluation results showing that GPT-4 Turbo provided correct answers with sound reasoning in a significant percentage of cases . This suggests that LLMs possibly possess a significant ability to reason and generalize .

  4. Performance Enhancements: The paper highlights the substantial performance gains achieved by LLMs in solving crossword puzzles, with GPT-4 Turbo achieving an impressive accuracy of 93% on Monday New York Times crosswords when aided by the algorithm developed in the study . This algorithm enables massive performance gains even when the baseline LLM accuracy is low, indicating the potential for LLMs to successfully solve crosswords when paired with the right search strategy .

In conclusion, the paper's innovative algorithm, enhanced performance in solving crossword puzzles, generalizability, and reasoning abilities of LLMs mark significant advancements in the field of crossword solving compared to previous methods, showcasing the potential for LLMs to bridge the gap between human experts and automated solvers in the near future.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of solving crossword puzzles using Large Language Models (LLMs). Noteworthy researchers in this area include Soumadeep Saha, Sutanoya Chakraborty, Saptarshi Saha, and Utpal Garain from the Indian Statistical Institute in Kolkata, India . One key aspect of the solution mentioned in the paper is the ability of LLMs to exploit constraints provided by partially filled grids in crossword puzzles to improve their accuracy in solving clues . This ability allows LLMs to incorporate character constraints from partially filled grids and generate more accurate answers, showcasing their proficiency in leveraging contextual information to enhance performance in crossword solving tasks.


How were the experiments in the paper designed?

The experiments in the paper "Language Models are Crossword Solvers" were designed to investigate the abilities of Large Language Models (LLMs) in solving crossword puzzles, particularly focusing on deciphering cryptic crossword clues and filling in full crossword grids . The experiments aimed to understand the strengths and weaknesses demonstrated by state-of-the-art (SoTA) LLMs in this multi-faceted task .

The experiments involved testing LLMs on various tasks such as sub-token counting, crossword clue deciphering, and grid-filling with constraints . The study analyzed the performance of LLMs in adhering to length constraints, counting the number of characters in a word, and exploiting constraints from partially filled grids to improve accuracy .

Additionally, the experiments included testing LLMs on different datasets like the New York Times (NYT), Cryptonite, and word-init-disjoint datasets with varying shot prompts to evaluate their performance . The experiments aimed to compare the performance of LLMs with previous state-of-the-art results and assess their ability to exploit constraints to enhance crossword solving capabilities .

Overall, the experiments were meticulously designed to assess the proficiency of LLMs in solving crossword puzzles, including cryptic clues, and to analyze the impact of constraints, dataset prevalence, and shot prompts on the performance of these language models .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on solving crossword puzzles with Large Language Models (LLMs) includes three main datasets:

  • A dataset curated from the New York Times (NYT) for straight or American style crosswords.
  • Cryptonite dataset by Efrat et al. (2021) for cryptic crosswords.
  • Word-init-disjoint dataset by Rozner et al. (2021) for cryptic crosswords .

Regarding the code, in adherence to ACL ethical guidelines, all scientific artifacts generated for the study, including software, prompts, raw model outputs, and data, have been made freely available and open source under the MIT license. However, the New York Times crossword dataset used in the study is not distributed as it is the intellectual property of The New York Times. The researchers purchased a subscription to access the puzzle repository but used the data without consent for the study, falling within the terms of the Fair Use doctrine set within 17 U.S.C. §107 .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study extensively analyzed the abilities of Large Language Models (LLMs) in solving crossword puzzles, focusing on deciphering cryptic crossword clues and filling in full crossword grids . The research demonstrated that current-generation state-of-the-art (SoTA) LLMs exhibit significant competence in deciphering cryptic crossword clues, surpassing previous SoTA results by a factor of 2-3 in relevant benchmarks . This indicates a substantial improvement in LLM performance in solving crossword puzzles.

The paper introduced a search algorithm that leverages the LLMs' performance to address the challenge of solving full crossword grids, achieving an impressive accuracy of 93% on New York Times crossword puzzles . This algorithm, named SweepClip, utilizes constraints imposed by previously generated answers to enhance future answers, showcasing the LLMs' capability to exploit constraints effectively . The results of this algorithm demonstrate a significant advancement in solving crossword puzzles with LLMs.

Furthermore, the study highlighted that the gap between LLM performance and human expert performance in solving crossword puzzles is narrower than previously concluded . By showcasing the strengths and weaknesses of SoTA LLMs in reasoning, wordplay, and understanding contextual clues, the research provides valuable insights into the capabilities of LLMs in tackling complex linguistic tasks like crossword solving . The experiments conducted in the paper offer substantial evidence to support the scientific hypotheses under investigation.


What are the contributions of this paper?

The paper on Language Models as Crossword Solvers makes several significant contributions:

  • It demonstrates that current state-of-the-art (SoTA) language models show substantial competence in deciphering cryptic crossword clues, surpassing previous SoTA results by a factor of 2-3 in relevant benchmarks .
  • The paper introduces a search algorithm that leverages the performance of language models to solve full crossword grids for the first time, achieving an impressive accuracy of 93% on New York Times crossword puzzles .
  • Contrary to prior research indicating a significant gap between language models and human experts in crossword solving, this paper suggests that the performance difference is narrower than previously thought .
  • The study delves into the abilities of Large Language Models (LLMs) in solving crossword puzzles, aiming to understand the strengths and weaknesses exhibited by SoTA LLMs .
  • It highlights that solving crossword puzzles demands high proficiency in contextual clues, wordplay, world knowledge, and reasoning, showcasing the diverse skills required for successful crossword solving .

What work can be continued in depth?

Further research in the field of solving crossword puzzles with Large Language Models (LLMs) can be extended in several ways based on the existing work:

  • Exploring Full Crossword Grid Solutions: Current research has focused on deciphering individual clues, but there is potential to delve deeper into solving full crossword grids by incorporating grid information and constraints imposed by them .
  • Improving Performance Gap with Human Experts: While recent studies have shown a significant performance gap between LLMs and human experts in solving cryptic crosswords, there is room for further investigation to narrow this gap by enhancing the algorithms and methodologies used .
  • Enhancing Few-Shot Prompt Performance: Experimenting with different prompt formats and protocols, such as varying the number of hints provided in few-shot prompts, can help in understanding the impact on the performance of LLMs in solving crossword puzzles .
  • Addressing Limitations and Data Contamination: Researchers can focus on overcoming key limitations highlighted in recent works, such as the use of extremely limited sets of LLMs and potential data contamination, to ensure more robust and accurate results in crossword solving tasks .
  • Incorporating New LLM Models: Continuously evaluating the performance of new LLM models like Mistral-7B, LLaMA2-7B, and ChatGPT 2 in solving crossword puzzles can provide insights into the advancements and capabilities of these models in tackling complex wordplay challenges .

Tables

6

Introduction
Background
Evolution of crossword puzzle-solving techniques
Emergence of large language models in NLP
Objective
To assess LLM performance in crossword puzzles
Compare GPT-4 Turbo with previous models
Investigate potential for human-level competition
Method
Data Collection
Selection of crosswords: American and cryptic styles
Source: New York Times puzzles and other benchmark sets
Data Preprocessing
Formatting puzzles for LLM input
Standardizing puzzle types and difficulty levels
Model Evaluation
Performance metrics: accuracy, speed, and problem-solving strategies
Human expert comparison
LLM Performance: Cryptic Puzzles
GPT-4 Turbo Outperforms Previous Models
Accuracy improvements over time
Breakdown of strengths and weaknesses
SweepClip Algorithm
Development and implementation
Impact on accuracy and efficiency
American Crossword Puzzles
LLM capabilities in non-cryptic formats
Challenges and limitations specific to these puzzles
Limitations and Areas for Improvement
Character counting difficulties
Grid information and spatial reasoning
Integration of context and wordplay
Future Directions
Potential for further advancements in LLMs
Implications for crossword-solving technology
Ethical and societal considerations
Conclusion
Summary of findings
The role of LLMs in crossword-solving landscape
Implications for human-computer collaboration in puzzle-solving.
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the accuracy rate achieved by the SweepClip algorithm on New York Times puzzles?
What types of crossword puzzles do the authors focus on in their paper?
What aspects of natural language understanding does the study highlight LLMs' progress in?
How does GPT-4 Turbo perform compared to previous models in solving cryptic puzzles?

Language Models are Crossword Solvers

Soumadeep Saha, Sutanoya Chakraborty, Saptarshi Saha, Utpal Garain·June 13, 2024

Summary

This paper investigates the capabilities of large language models (LLMs) in solving crossword puzzles, focusing on both American and cryptic styles. The authors find that current LLMs, like GPT-4 Turbo, significantly outperform previous models in cryptic puzzles, closing the gap with human experts by 2-3 times. They develop the SweepClip algorithm, which integrates LLMs into a full crossword-solving process, achieving 93% accuracy on New York Times puzzles. The study highlights LLMs' progress in natural language understanding, wordplay, and constraint satisfaction, but also identifies limitations like difficulty with character counting and the need for grid information. The research suggests that LLMs are becoming competitive with human solvers and could further improve with future advancements.
Mind map
Human expert comparison
Performance metrics: accuracy, speed, and problem-solving strategies
Integration of context and wordplay
Grid information and spatial reasoning
Character counting difficulties
Impact on accuracy and efficiency
Development and implementation
Breakdown of strengths and weaknesses
Accuracy improvements over time
Model Evaluation
Source: New York Times puzzles and other benchmark sets
Selection of crosswords: American and cryptic styles
Investigate potential for human-level competition
Compare GPT-4 Turbo with previous models
To assess LLM performance in crossword puzzles
Emergence of large language models in NLP
Evolution of crossword puzzle-solving techniques
Implications for human-computer collaboration in puzzle-solving.
The role of LLMs in crossword-solving landscape
Summary of findings
Ethical and societal considerations
Implications for crossword-solving technology
Potential for further advancements in LLMs
Limitations and Areas for Improvement
SweepClip Algorithm
GPT-4 Turbo Outperforms Previous Models
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Directions
American Crossword Puzzles
LLM Performance: Cryptic Puzzles
Method
Introduction
Outline
Introduction
Background
Evolution of crossword puzzle-solving techniques
Emergence of large language models in NLP
Objective
To assess LLM performance in crossword puzzles
Compare GPT-4 Turbo with previous models
Investigate potential for human-level competition
Method
Data Collection
Selection of crosswords: American and cryptic styles
Source: New York Times puzzles and other benchmark sets
Data Preprocessing
Formatting puzzles for LLM input
Standardizing puzzle types and difficulty levels
Model Evaluation
Performance metrics: accuracy, speed, and problem-solving strategies
Human expert comparison
LLM Performance: Cryptic Puzzles
GPT-4 Turbo Outperforms Previous Models
Accuracy improvements over time
Breakdown of strengths and weaknesses
SweepClip Algorithm
Development and implementation
Impact on accuracy and efficiency
American Crossword Puzzles
LLM capabilities in non-cryptic formats
Challenges and limitations specific to these puzzles
Limitations and Areas for Improvement
Character counting difficulties
Grid information and spatial reasoning
Integration of context and wordplay
Future Directions
Potential for further advancements in LLMs
Implications for crossword-solving technology
Ethical and societal considerations
Conclusion
Summary of findings
The role of LLMs in crossword-solving landscape
Implications for human-computer collaboration in puzzle-solving.
Key findings
11

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of solving crossword puzzles with the assistance of Language Models (LLMs) by proposing an algorithm called SweepClip . This problem involves not only generating correct answers from provided clues but also leveraging constraints from previously generated words and back-tracking to eliminate incorrect answers when new information becomes available . While traditional approaches to solving crosswords involve candidate answer proposal systems and grid-filling algorithms, the use of LLMs in this context presents a new high bar for AI systems . The paper explores the performance of LLMs in solving crossword puzzles, highlighting the potential for LLMs to excel in this task with future advancements .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that language models (LLMs) have the ability to count sub-tokens based on information provided during training, specifically focusing on the task of sub-token counting . The study aims to investigate whether LLMs can accurately count the number of characters in a word or phrase, and how their performance in this task varies based on the frequency of the token . The hypothesis is centered around understanding how LLMs handle sub-token counting and whether they can generalize this ability across words with different prevalence levels .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Language Models are Crossword Solvers" proposes several innovative ideas, methods, and models in the realm of solving crossword puzzles using Large Language Models (LLMs) . One key contribution is the demonstration of the significant competence of current-generation state-of-the-art (SoTA) language models in deciphering cryptic crossword clues, surpassing previous SoTA results by a factor of 2-3 in relevant benchmarks . The paper introduces a search algorithm that leverages the performance of LLMs to solve full crossword grids for the first time, achieving an impressive accuracy of 93% on New York Times crossword puzzles .

The paper delves into the analysis of LLMs' abilities to solve crossword puzzles, aiming to understand the strengths and weaknesses exhibited by SoTA LLMs . It highlights that solving crossword puzzles necessitates high proficiency in contextual clue understanding, wordplay, world knowledge, and reasoning . The research explores various skills involved in solving cryptic crossword clues, such as connecting clues to relevant knowledge, reasoning, and wordplay . Additionally, the paper emphasizes the importance of adherence to character and length constraints in crossword solving .

One novel aspect introduced in the paper is the development of a search algorithm, named SweepClip, which significantly enhances the performance of LLMs in solving crossword puzzles . This algorithm improves clue-wise answer accuracy and boosts the overall crossword solving accuracy by exploiting constraint information to a greater extent than traditional question-answering approaches . SweepClip implicitly conducts self-consistency checks to refine candidate answers, leading to improved performance in solving crosswords .

Furthermore, the paper presents results that showcase the substantial performance gains achieved by current-generation SoTA LLMs in deciphering cryptic crossword clues . By employing innovative prompting techniques like Chain-of-thought prompting with self-consistency, the paper demonstrates enhanced performance in solving cryptic crossword clues compared to previous SoTA results . This approach leads to a notable improvement in accuracy without the need for fine-tuning . The paper "Language Models are Crossword Solvers" introduces novel characteristics and advantages compared to previous methods in solving crossword puzzles using Large Language Models (LLMs) . Here are some key points:

  1. Significant Performance Improvements: The current generation of state-of-the-art (SoTA) LLMs exhibit remarkable proficiency in solving both straight and cryptic crossword puzzle clues, surpassing previous results by a substantial margin . These LLMs demonstrate enhanced aptitude in deciphering cryptic crossword clues, with GPT-4 Turbo achieving an accuracy of 18.70% on the cryptic crossword task, showcasing a significant improvement over previous SoTA results .

  2. Innovative Algorithm - SweepClip: The paper introduces an innovative search algorithm called SweepClip, which significantly enhances the performance of LLMs in solving crossword puzzles . This algorithm improves clue-wise answer accuracy and boosts overall crossword solving accuracy by exploiting constraint information more effectively than traditional question-answering approaches . SweepClip implicitly conducts self-consistency checks to refine candidate answers, leading to improved performance in solving crosswords .

  3. Generalizability and Reasoning Abilities: The research delves into the generalizability of LLMs beyond potential data contamination in their training set . The models demonstrate the capacity to reason about unseen cryptic crossword clues and arrive at correct responses, as evidenced by human evaluation results showing that GPT-4 Turbo provided correct answers with sound reasoning in a significant percentage of cases . This suggests that LLMs possibly possess a significant ability to reason and generalize .

  4. Performance Enhancements: The paper highlights the substantial performance gains achieved by LLMs in solving crossword puzzles, with GPT-4 Turbo achieving an impressive accuracy of 93% on Monday New York Times crosswords when aided by the algorithm developed in the study . This algorithm enables massive performance gains even when the baseline LLM accuracy is low, indicating the potential for LLMs to successfully solve crosswords when paired with the right search strategy .

In conclusion, the paper's innovative algorithm, enhanced performance in solving crossword puzzles, generalizability, and reasoning abilities of LLMs mark significant advancements in the field of crossword solving compared to previous methods, showcasing the potential for LLMs to bridge the gap between human experts and automated solvers in the near future.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of solving crossword puzzles using Large Language Models (LLMs). Noteworthy researchers in this area include Soumadeep Saha, Sutanoya Chakraborty, Saptarshi Saha, and Utpal Garain from the Indian Statistical Institute in Kolkata, India . One key aspect of the solution mentioned in the paper is the ability of LLMs to exploit constraints provided by partially filled grids in crossword puzzles to improve their accuracy in solving clues . This ability allows LLMs to incorporate character constraints from partially filled grids and generate more accurate answers, showcasing their proficiency in leveraging contextual information to enhance performance in crossword solving tasks.


How were the experiments in the paper designed?

The experiments in the paper "Language Models are Crossword Solvers" were designed to investigate the abilities of Large Language Models (LLMs) in solving crossword puzzles, particularly focusing on deciphering cryptic crossword clues and filling in full crossword grids . The experiments aimed to understand the strengths and weaknesses demonstrated by state-of-the-art (SoTA) LLMs in this multi-faceted task .

The experiments involved testing LLMs on various tasks such as sub-token counting, crossword clue deciphering, and grid-filling with constraints . The study analyzed the performance of LLMs in adhering to length constraints, counting the number of characters in a word, and exploiting constraints from partially filled grids to improve accuracy .

Additionally, the experiments included testing LLMs on different datasets like the New York Times (NYT), Cryptonite, and word-init-disjoint datasets with varying shot prompts to evaluate their performance . The experiments aimed to compare the performance of LLMs with previous state-of-the-art results and assess their ability to exploit constraints to enhance crossword solving capabilities .

Overall, the experiments were meticulously designed to assess the proficiency of LLMs in solving crossword puzzles, including cryptic clues, and to analyze the impact of constraints, dataset prevalence, and shot prompts on the performance of these language models .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on solving crossword puzzles with Large Language Models (LLMs) includes three main datasets:

  • A dataset curated from the New York Times (NYT) for straight or American style crosswords.
  • Cryptonite dataset by Efrat et al. (2021) for cryptic crosswords.
  • Word-init-disjoint dataset by Rozner et al. (2021) for cryptic crosswords .

Regarding the code, in adherence to ACL ethical guidelines, all scientific artifacts generated for the study, including software, prompts, raw model outputs, and data, have been made freely available and open source under the MIT license. However, the New York Times crossword dataset used in the study is not distributed as it is the intellectual property of The New York Times. The researchers purchased a subscription to access the puzzle repository but used the data without consent for the study, falling within the terms of the Fair Use doctrine set within 17 U.S.C. §107 .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study extensively analyzed the abilities of Large Language Models (LLMs) in solving crossword puzzles, focusing on deciphering cryptic crossword clues and filling in full crossword grids . The research demonstrated that current-generation state-of-the-art (SoTA) LLMs exhibit significant competence in deciphering cryptic crossword clues, surpassing previous SoTA results by a factor of 2-3 in relevant benchmarks . This indicates a substantial improvement in LLM performance in solving crossword puzzles.

The paper introduced a search algorithm that leverages the LLMs' performance to address the challenge of solving full crossword grids, achieving an impressive accuracy of 93% on New York Times crossword puzzles . This algorithm, named SweepClip, utilizes constraints imposed by previously generated answers to enhance future answers, showcasing the LLMs' capability to exploit constraints effectively . The results of this algorithm demonstrate a significant advancement in solving crossword puzzles with LLMs.

Furthermore, the study highlighted that the gap between LLM performance and human expert performance in solving crossword puzzles is narrower than previously concluded . By showcasing the strengths and weaknesses of SoTA LLMs in reasoning, wordplay, and understanding contextual clues, the research provides valuable insights into the capabilities of LLMs in tackling complex linguistic tasks like crossword solving . The experiments conducted in the paper offer substantial evidence to support the scientific hypotheses under investigation.


What are the contributions of this paper?

The paper on Language Models as Crossword Solvers makes several significant contributions:

  • It demonstrates that current state-of-the-art (SoTA) language models show substantial competence in deciphering cryptic crossword clues, surpassing previous SoTA results by a factor of 2-3 in relevant benchmarks .
  • The paper introduces a search algorithm that leverages the performance of language models to solve full crossword grids for the first time, achieving an impressive accuracy of 93% on New York Times crossword puzzles .
  • Contrary to prior research indicating a significant gap between language models and human experts in crossword solving, this paper suggests that the performance difference is narrower than previously thought .
  • The study delves into the abilities of Large Language Models (LLMs) in solving crossword puzzles, aiming to understand the strengths and weaknesses exhibited by SoTA LLMs .
  • It highlights that solving crossword puzzles demands high proficiency in contextual clues, wordplay, world knowledge, and reasoning, showcasing the diverse skills required for successful crossword solving .

What work can be continued in depth?

Further research in the field of solving crossword puzzles with Large Language Models (LLMs) can be extended in several ways based on the existing work:

  • Exploring Full Crossword Grid Solutions: Current research has focused on deciphering individual clues, but there is potential to delve deeper into solving full crossword grids by incorporating grid information and constraints imposed by them .
  • Improving Performance Gap with Human Experts: While recent studies have shown a significant performance gap between LLMs and human experts in solving cryptic crosswords, there is room for further investigation to narrow this gap by enhancing the algorithms and methodologies used .
  • Enhancing Few-Shot Prompt Performance: Experimenting with different prompt formats and protocols, such as varying the number of hints provided in few-shot prompts, can help in understanding the impact on the performance of LLMs in solving crossword puzzles .
  • Addressing Limitations and Data Contamination: Researchers can focus on overcoming key limitations highlighted in recent works, such as the use of extremely limited sets of LLMs and potential data contamination, to ensure more robust and accurate results in crossword solving tasks .
  • Incorporating New LLM Models: Continuously evaluating the performance of new LLM models like Mistral-7B, LLaMA2-7B, and ChatGPT 2 in solving crossword puzzles can provide insights into the advancements and capabilities of these models in tackling complex wordplay challenges .
Tables
6
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