Summary of Llms For Relational Reasoning: How Far Are We?, by Zhiming Li et al.
LLMs for Relational Reasoning: How Far are We?
by Zhiming Li, Yushi Cao, Xiufeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-wei Lin, Yang Liu
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Large language models (LLMs) have shown impressive results on various downstream tasks. However, it’s unclear whether they truly possess strong reasoning abilities. Previous benchmarks were limited to simple, shallow challenges. Recent studies found LLMs struggled with sequential decision-making problems requiring common-sense planning. To assess their reasoning abilities more thoroughly, we employed the inductive logic programming (ILP) benchmark, a challenging test of logic program induction and synthesis systems. Our findings show that state-of-the-art LLMs performed poorly compared to smaller neural program induction systems, even when using natural language prompts or truth-value matrices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many things well, like understand and generate text. But can they really think critically? Some people think these models are smart because they’re good at simple tasks. However, new research is showing that they might not be as clever as we thought. In fact, when faced with harder problems, they struggle to make decisions. This study tested the language models using a special test called inductive logic programming (ILP). The results show that these powerful models don’t do as well as smaller ones at making logical connections and solving problems. |
