Summary of Carl-gt: Evaluating Causal Reasoning Capabilities Of Large Language Models, by Ruibo Tu et al.
CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models
by Ruibo Tu, Hedvig Kjellström, Gustav Eje Henter, Cheng Zhang
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 This paper proposes a benchmark for evaluating large language models (LLMs) in terms of their causal reasoning capabilities. The current benchmarks are mainly based on conversational tasks, academic math tests, and coding tests, which evaluate LLMs in well-regularized settings but are limited in assessing their ability to solve real-world problems. The proposed CARL-GT benchmark evaluates LLMs’ ability to reason causally using graphs and tabular data, covering diverse tasks such as causal graph reasoning, knowledge discovery, and decision-making. Zero-shot learning prompts are developed for the tasks, and experiments are conducted to evaluate open-source LLMs, revealing that they are still weak in casual reasoning, especially with tabular data. The results also show that different benchmark tasks have stronger correlations across categories than within categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to test how well large language models can reason and solve problems. Right now, there aren’t many ways to do this, so the creators of this model made a new one called CARL-GT. It tests the model’s ability to understand cause-and-effect relationships using graphs and tables. The benchmark has different tasks that check the model’s skills in areas like finding new knowledge, making decisions, and understanding causal relationships. The results show that current language models are not very good at this kind of problem-solving, especially when dealing with complex data. |
Keywords
» Artificial intelligence » Zero shot
