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@@ -7,3 +7,23 @@ This repository contains the `NYT-Connections` dataset proposed in the work *NYT
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Authors: Angel Yahir Loredo Lopez, Tyler McDonald, Ali Emami
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## Paper Abstract
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Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. We present NYT-Connections, a collection of 358 simple word classification puzzles derived from the New York Times Connections game. This benchmark is designed to penalize quick, intuitive ``System 1'' thinking, isolating fundamental reasoning skills. We evaluated six recent LLMs, a simple machine learning heuristic, and humans across three configurations: single-attempt, multiple attempts without hints, and multiple attempts with contextual hints. Our findings reveal a significant performance gap: even top-performing LLMs like GPT-4 fall short of human performance by nearly 30\%. Notably, advanced prompting techniques such as Chain-of-Thought and Self-Consistency show diminishing returns as task difficulty increases. NYT-Connections uniquely combines linguistic isolation, resistance to intuitive shortcuts, and regular updates to mitigate data leakage, offering a novel tool for assessing LLM reasoning capabilities.
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Authors: Angel Yahir Loredo Lopez, Tyler McDonald, Ali Emami
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## Paper Abstract
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Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. We present NYT-Connections, a collection of 358 simple word classification puzzles derived from the New York Times Connections game. This benchmark is designed to penalize quick, intuitive ``System 1'' thinking, isolating fundamental reasoning skills. We evaluated six recent LLMs, a simple machine learning heuristic, and humans across three configurations: single-attempt, multiple attempts without hints, and multiple attempts with contextual hints. Our findings reveal a significant performance gap: even top-performing LLMs like GPT-4 fall short of human performance by nearly 30\%. Notably, advanced prompting techniques such as Chain-of-Thought and Self-Consistency show diminishing returns as task difficulty increases. NYT-Connections uniquely combines linguistic isolation, resistance to intuitive shortcuts, and regular updates to mitigate data leakage, offering a novel tool for assessing LLM reasoning capabilities.
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## Puzzle Description
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*NYT-Connections* puzzles are a subset of the New York Times' daily *Connections* contests. Each puzzle consists of 16 words, with the goal being to sort these words into 4 correct groupings of varying difficulty. The base game allows for hints when a solution is one word away from being a correct group, and allows up to 4 mistakes. Thus, the goal is to correctly identify all 4 groups without committing 4 mistakes.
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## Data Description
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`date` - the original date the contest was offered.
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`contest` - the title string for the contest.
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`words` - the collection of 16 words available for use in puzzle solving.
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`answers` - an array of objects, where each object is a correct group and contains:
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- `answerDescription` - the group name
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- `words` - the 4 words that classify into this group
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`difficulty` - the difficulty of the puzzle as rated by community contributors.
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## Citation
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*To be added.*
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