Summary of Benchmarking and Understanding Compositional Relational Reasoning Of Llms, by Ruikang Ni et al.
Benchmarking and Understanding Compositional Relational Reasoning of LLMs
by Ruikang Ni, Da Xiao, Qingye Meng, Xiangyu Li, Shihui Zheng, Hongliang Liang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 explores the compositional relational reasoning (CRR) capabilities of large language models (LLMs), specifically transformer-based models. The authors propose a new synthetic benchmark, Generalized Associative Recall (GAR), which integrates various mechanistic interpretability (MI) tasks to challenge LLMs’ CRR abilities. Evaluation shows that existing LLMs struggle with GAR, revealing their fundamental limitations in solving CRR tasks. To understand how LLMs solve GAR tasks, the authors use attribution patching to identify core circuits reused by Vicuna-33B across different tasks and attention heads. Intervention experiments demonstrate that specific attention heads play a crucial role in task performance, particularly two classes of heads representing true and false notions in GAR tasks. These findings have significant implications for our understanding of LLMs’ CRR capabilities and their applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models can understand relationships between things. It creates a new test to see if these models are good at this, called Generalized Associative Recall (GAR). The results show that current models don’t do very well on this test, which means they have trouble understanding relationships. To figure out why the models struggle, researchers use a special tool to identify important parts of the model’s internal workings. They found that certain parts of the model are crucial for its ability to understand relationships. This is important because it can help us make better language models that can do more complicated tasks. |
Keywords
» Artificial intelligence » Attention » Recall » Transformer
