| Challenge: | Recent research suggests that solving cryptic crosswords is challenging even for modern NLP models, including Large Language Models (LLMs). |
| Approach: | They establish benchmark results for three popular LLMs: Gemma2, LLaMA3 and ChatGPT, and investigate why these models struggle to achieve superior performance. |
| Outcome: | The proposed models perform significantly below humans on the cryptic crossword puzzle task, while human solvers achieve 99% accuracy. |
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Language Models are Crossword Solvers (2025.naacl-long)
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| Challenge: | Modern crossword models demonstrate astounding skills in reasoning, coding, wordplay, question answering, and a multitude of other tasks. |
| Approach: | They propose a search algorithm that generalizes well and can support answers with sound rationale by solving full crossword grids with out-of-the-box LLMs. |
| Outcome: | The proposed model outperforms state-of-the-art models in solving crossword grids for the first time and generalizes well. |
CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenge (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities, but their capabilities in cryptographic decryption tasks remain underexplored. |
| Approach: | They propose a benchmark to evaluate the reasoning capabilities of large language models in cryptographic decryption tasks. |
| Outcome: | The proposed benchmark examines the reasoning capabilities of large language models in cryptographic decryption tasks. |
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language (2021.emnlp-main)
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| Challenge: | Current datasets targeting ambiguity can be solved by a native speaker with relative ease. |
| Approach: | They present a large-scale dataset based on cryptic crosswords with a cryptical clue. |
| Outcome: | The proposed dataset is based on cryptic crosswords with 523K examples. |
Puzzle Solving using Reasoning of Large Language Models: A Survey (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their logical reasoning abilities across various domains. |
| Approach: | They propose to divide puzzles into rule-based and rule-less categories and critically assess LLMs' performance through various methodologies. |
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Do You Get the Hint? Benchmarking LLMs on the Board Game Concept (2026.findings-acl)
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| Challenge: | Large language models have achieved impressive progress on many benchmarks, yet they still have fundamental weaknesses. |
| Approach: | They introduce Concept, a word-guessing board game, as a benchmark for probing abductive reasoning. |
| Outcome: | The proposed game is easily solved by humans, but is still very challenging for state-of-the-art LLMs (no model exceeds 40% success rate). |
Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models (2023.emnlp-main)
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| Challenge: | The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past decade. |
| Approach: | They propose a benchmark dataset for evaluating the problem solving abilities of large language models (LLMs) they curate 515 challenging problems from the highly competitive IIT JEE-Advanced exam. |
| Outcome: | The proposed model performs better on open-source and proprietary models than the current model, but with techniques like self-consistency, self-refinement and chain-of-thought prompting. |
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages (2023.emnlp-main)
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| Challenge: | Existing instruction tuned large language models (LLMs) struggle to understand cross-lingual sociopragmatic meaning (SM) lack of comprehensive investigation into their ability to understand SM is partly due to SM not being adequately represented in any of the existing benchmarks. |
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There’s No Such Thing as Simple Reasoning for LLMs (2025.findings-acl)
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| Challenge: | Existing work has focused on relatively complex “many-hop” reasoning problems. |
| Approach: | They analyse the performance of fine-tuned LLMs on simple reasoning problems . they find the models remain highly brittle, being susceptible to seemingly innocent perturbations . |
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Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)
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| Challenge: | Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models. |
| Approach: | They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance. |
| Outcome: | The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities. |
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)
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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua
| Challenge: | Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown. |
| Approach: | They propose a benchmark for evaluating large language models using a well-organized taxonomy. |
| Outcome: | The proposed model is based on a well-organized taxonomy and compares it with other models. |