<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://puzzlellms.github.io/</id><title>PuzzleLLMs</title><subtitle>A Survey on Puzzle Solving using Reasoning of Large Language Models </subtitle> <updated>2024-04-20T11:45:04+00:00</updated> <author> <name></name> <uri>https://puzzlellms.github.io/</uri> </author><link rel="self" type="application/atom+xml" href="https://puzzlellms.github.io/feed.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://puzzlellms.github.io/"/> <generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator> <rights> © 2024 </rights> <icon>/assets/img/favicons/favicon.ico</icon> <logo>/assets/img/favicons/favicon-96x96.png</logo> <entry><title>PokerGPT</title><link href="https://puzzlellms.github.io/posts/PokerGPT/" rel="alternate" type="text/html" title="PokerGPT" /><published>2024-01-03T16:00:00+00:00</published> <updated>2024-03-20T17:06:01+00:00</updated> <id>https://puzzlellms.github.io/posts/PokerGPT/</id> <content src="https://puzzlellms.github.io/posts/PokerGPT/" /> <author> <name></name> </author> <category term="dataset" /> <category term="stochastic" /> <summary> 📙Paper: “PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold’em via Large Language Model” 🔑Public: ✅ ⚲ Area: Dataset, Stochastic 📅 Date: 2024-01-04 🔎 Paper Section: dataset / rule-based / stochastic 📝 References: 29 </summary> </entry> <entry><title>Mathematical Language Models</title><link href="https://puzzlellms.github.io/posts/Mathematical-Language-Models/" rel="alternate" type="text/html" title="Mathematical Language Models" /><published>2023-12-11T16:00:00+00:00</published> <updated>2024-03-20T17:06:01+00:00</updated> <id>https://puzzlellms.github.io/posts/Mathematical-Language-Models/</id> <content src="https://puzzlellms.github.io/posts/Mathematical-Language-Models/" /> <author> <name></name> </author> <category term="mathematical language models" /> <summary> 📙Paper: “Mathematical Language Models: A Survey” 🔑Public: ✅ ⚲ Area: Mathematical language models 📅 Date: 2023-12-12 🔎 Paper Section: Introduction 📝 References: 213 </summary> </entry> <entry><title>Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs</title><link href="https://puzzlellms.github.io/posts/Neuro-Symbolic-Integration-Brings-Causal-and-Reliable-Reasoning-Proofs/" rel="alternate" type="text/html" title="Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs" /><published>2023-11-15T16:00:00+00:00</published> <updated>2024-03-20T17:06:01+00:00</updated> <id>https://puzzlellms.github.io/posts/Neuro-Symbolic-Integration-Brings-Causal-and-Reliable-Reasoning-Proofs/</id> <content src="https://puzzlellms.github.io/posts/Neuro-Symbolic-Integration-Brings-Causal-and-Reliable-Reasoning-Proofs/" /> <author> <name></name> </author> <category term="neuro-symbolic" /> <summary> 📙Paper: “Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs” 🔑Public: ✅ ⚲ Area: Neuro-symbolic 📅 Date: 2023-11-16 🔎 Paper Section: methods / puzzle translation 📝 References: 26 </summary> </entry> <entry><title>LLMs cannot find reasoning errors, but can correct them</title><link href="https://puzzlellms.github.io/posts/LLMs-cannot-find-reasoning-errors,-but-can-correct-them/" rel="alternate" type="text/html" title="LLMs cannot find reasoning errors, but can correct them" /><published>2023-11-13T16:00:00+00:00</published> <updated>2024-03-20T17:06:01+00:00</updated> <id>https://puzzlellms.github.io/posts/LLMs-cannot-find-reasoning-errors,-but-can-correct-them/</id> <content src="https://puzzlellms.github.io/posts/LLMs-cannot-find-reasoning-errors,-but-can-correct-them/" /> <author> <name></name> </author> <category term="prompting" /> <category term="self-correction" /> <summary> 📙Paper: “LLMs cannot find reasoning errors, but can correct them!” 🔑Public: ✅ ⚲ Area: Prompting, Self-correction 📅 Date: 2023-11-14 🔎 Paper Section: methods / advanced / self-correction 📝 References: 22 </summary> </entry> <entry><title>Assessing Logical Puzzle Solving in Large Language Models</title><link href="https://puzzlellms.github.io/posts/Assessing-Logical-Puzzle-Solving-in-Large-Language-Models/" rel="alternate" type="text/html" title="Assessing Logical Puzzle Solving in Large Language Models" /><published>2023-11-12T16:00:00+00:00</published> <updated>2024-03-20T17:06:01+00:00</updated> <id>https://puzzlellms.github.io/posts/Assessing-Logical-Puzzle-Solving-in-Large-Language-Models/</id> <content src="https://puzzlellms.github.io/posts/Assessing-Logical-Puzzle-Solving-in-Large-Language-Models/" /> <author> <name></name> </author> <category term="dataset" /> <category term="stochastic" /> <summary> 📙Paper: “Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study” 🔑Public: ✅ ⚲ Area: Dataset, Stochastic 📅 Date: 2023-11-13 🔎 Paper Section: dataset / rule-based / stochastic 📝 References: - </summary> </entry> </feed>
