PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction (2025.findings-naacl)
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| Challenge: | PuzzleGPT is a modular and iterative puzzlesolving method for predicting time and location from images. |
| Approach: | They propose to formalize this ability into core skills and implement it using different modules in an expert pipeline called PuzzleGPT. |
| Outcome: | The proposed method outperforms large VLMs and finetuned models on TARA and WikiTilo and rivals or surpasses finetuned models. |
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| Challenge: | Existing vision-language models lack spatial reasoning capability, despite their ability to comprehend spatial arrangements and model structural relations. |
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Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023.emnlp-main)
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| Challenge: | Recent advances in scale have enabled large language models to perform NLP tasks zero-shot . however, it is not known whether ChatGPT can serve as a generalist model that can perform many NLP jobs zero- shot. |
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| Challenge: | In the months since its release, ChatGPT and its underlying model, GPT3.5, have garnered massive attention due to their potent mix of capability and accessibility. |
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ReportGPT: Human-in-the-loop Verifiable Table-to-Text Generation (2024.emnlp-industry)
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| Challenge: | Recent advances in the quality and accessibility of large language models have precipitated a surge in user-facing tools for content generation. |
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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. |
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There’s a Time and Place for Reasoning Beyond the Image (2022.acl-long)
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| Challenge: | Currently, most work in this area is focused on reasoning with local evidence, but there is a gap between a state-of-the-art joint model and human performance. |
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ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)
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Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? (2024.emnlp-main)
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Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
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Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren
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PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework (2025.acl-industry)
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| Challenge: | Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency. |
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