Summary of Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles, by Kulin Shah et al.
Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles
by Kulin Shah, Nishanth Dikkala, Xin Wang, Rina Panigrahy
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Causal language modeling using the Transformer architecture has achieved impressive results in Large Language Models (LLMs). However, it is unclear whether fundamental search and reasoning capabilities emerged within LLMs. This paper investigates whether causal language modeling can learn to solve complex tasks like Sudoku puzzles. To solve Sudoku, models must search for empty cells, decide which cell to fill, and then apply a strategy to fill the decided cell. The model may need to use multiple strategies to fill a single cell. Our Transformer model trained on this task can indeed learn to solve Sudokus (94.21% correct) when trained with logical sequence of steps taken by a solver. Without such training, it fails to learn Sudoku. We also extend our analysis to Zebra puzzles and find that the model solves 92.04% of puzzles correctly. Furthermore, we study the internal representations of the trained Transformer and find that linear probing can decode information about possible values in any given cell, indicating a strong reasoning engine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether computers can learn to solve complex puzzles like Sudoku and Zebra puzzles using language models. These models are good at understanding human language, but it’s not clear if they have the ability to search and reason like humans do. The researchers trained a computer model on a task that involves solving Sudoku puzzles, and found that when trained with logical steps taken by a human solver, the model can solve over 94% of puzzles correctly! They also tried this with Zebra puzzles and got similar results. When they looked at what’s going on inside the model, they found that it has a way to figure out possible values in each puzzle cell, which is like having a reasoning engine. |
Keywords
» Artificial intelligence » Transformer
