Summary of Bi-chainer: Automated Large Language Models Reasoning with Bidirectional Chaining, by Shuqi Liu et al.
Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining
by Shuqi Liu, Bowei He, Linqi Song
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: Large Language Models (LLMs) have achieved impressive reasoning abilities but still struggle with complex logical problems. The existing unidirectional chaining methods, such as forward and backward chaining, suffer from low prediction accuracy and efficiency issues. To address these limitations, we propose a bidirectional chaining method, Bi-Chainer, which dynamically switches to depth-first reasoning in the opposite direction when encountering multiple branching options. This enables the utilization of intermediate results as guidance for the reasoning process. Our experiments demonstrate that Bi-Chainer achieves significant accuracy boosts over unidirectional frameworks on four challenging logical reasoning datasets. Moreover, Bi-Chainer enhances the accuracy of intermediate proof steps and reduces the average number of inference calls, resulting in more efficient and accurate reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have been working on making computers think more like humans. They’ve made progress but still struggle with complex problems that require logical thinking. The current methods they use are not very good at solving these kinds of problems. To improve this, a new method called Bi-Chainer was developed. It’s like taking two different routes to the same destination and using what you learn along the way to make better decisions. This new method does much better than the old ones on four tricky problem sets. It also helps computers come up with better solutions faster. |
Keywords
» Artificial intelligence » Inference
