Related papers: Riddle Quest : The Enigma of Words

Riddle Quest : The Enigma of Words

URL: http://arxiv.org/abs/2601.19273v1
Date: Tue, 27 Jan 2026 07:03:29 GMT
Title: Riddle Quest : The Enigma of Words
Authors: Niharika Sri Parasa, Chaitali Diwan, Srinath Srinivasa,
Abstract summary: We introduce a simple pipeline for creating and evaluating analogy-based riddles.<n>The system includes a triples creator that builds structured facts about a concept, a semantic mapper that selects attributes useful for analogy, and a stylized generator that turns them into riddle clues.
Score: 2.523415604068924
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Riddles are concise linguistic puzzles that describe an object or idea through indirect, figurative, or playful clues. They are a longstanding form of creative expression, requiring the solver to interpret hints, recognize patterns, and draw inferences to identify the answers. In this work, we introduce a simple pipeline for creating and evaluating analogy-based riddles. The system includes a triples creator that builds structured facts about a concept, a semantic mapper that selects attributes useful for analogy, a stylized generator that turns them into riddle clues, and a validator that collects all possible answers the riddle could point to. We use this validator to study whether large language models can recover the full answer set for different riddle types. Our case study shows that while models often guess the main intended answer, they frequently miss other valid interpretations. This highlights the value of riddles as a lightweight tool for examining reasoning coverage and ambiguity handling in language models.

Related papers

A Reasoning-Based Approach to Cryptic Crossword Clue Solving [0.18416014644193066]
This work describes an LLM-based reasoning system built from open-licensed components.<n>It solves cryptic clues by (i) hypothesising answers; (ii) proposing wordplay explanations; and (iii) using a verifier system that operates on codified reasoning steps.<n>Overall, this system establishes a new state-of-the-art performance on the challenging Cryptonite dataset of clues from The Times and The Telegraph newspapers in the UK.
arXiv Detail & Related papers (2025-06-05T09:43:28Z)
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint [57.73346054360675]
Rebus puzzles, visual riddles that encode language through imagery, spatial arrangement, and symbolic substitution, pose a unique challenge to current vision-language models (VLMs)<n>In this paper, we investigate the capacity of contemporary VLMs to interpret and solve rebus puzzles by constructing a hand-generated and annotated benchmark of diverse English-language rebus puzzles.
arXiv Detail & Related papers (2025-05-29T17:59:47Z)
Logic-of-Thought: Empowering Large Language Models with Logic Programs for Solving Puzzles in Natural Language [67.51318974970985]
Solving puzzles in natural language poses a long-standing challenge in AI.<n>We propose Logic-of-Thought, a framework that bridges large language models with logic programming.<n>We evaluate our method on various grid puzzles and dynamic puzzles involving actions, demonstrating near-perfect accuracy across all tasks.
arXiv Detail & Related papers (2025-05-22T01:37:40Z)
Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models [25.732397636695882]
We introduce $textitTruthQuest$, a benchmark for suppositional reasoning based on the principles of knights and knaves puzzles. Evaluations show that large language models like Llama 3 and Mixtral-8x7B exhibit significant difficulties solving these tasks.
arXiv Detail & Related papers (2024-06-18T12:24:22Z)
Language Models can be Logical Solvers [99.40649402395725]
We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
arXiv Detail & Related papers (2023-11-10T16:23:50Z)
CC-Riddle: A Question Answering Dataset of Chinese Character Riddles [51.41044750575767]
The Chinese character riddle is a unique form of cultural entertainment specific to the Chinese language. We construct a textbfChinese textbfCharacter riddle dataset named CC-Riddle.
arXiv Detail & Related papers (2022-06-28T06:23:13Z)
BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles [82.63394952538292]
We introduce BiRdQA, a bilingual multiple-choice question answering dataset with 6614 English riddles and 8751 Chinese riddles. Existing monolingual and multilingual QA models fail to perform well on our dataset, indicating that there is a long way to go before machine can beat human on solving tricky riddles.
arXiv Detail & Related papers (2021-09-23T00:46:47Z)
Discrete Reasoning Templates for Natural Language Understanding [79.07883990966077]
We present an approach that reasons about complex questions by decomposing them to simpler subquestions. We derive the final answer according to instructions in a predefined reasoning template. We show that our approach is competitive with the state-of-the-art while being interpretable and requires little supervision.
arXiv Detail & Related papers (2021-04-05T18:56:56Z)
RiddleSense: Answering Riddle Questions as Commonsense Reasoning [35.574564653690594]
RiddleSense is a novel multiple-choice question answering challenge for benchmarking higher-order commonsense reasoning models. RiddleSense is the first large dataset for riddle-style commonsense question answering, where the distractors are crowdsourced from human annotators. We systematically evaluate a wide range of reasoning models over it and point out that there is a large gap between the best-supervised model and human performance.
arXiv Detail & Related papers (2021-01-02T05:28:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.