Summary of Aqa-bench: An Interactive Benchmark For Evaluating Llms’ Sequential Reasoning Ability, by Siwei Yang et al.
AQA-Bench: An Interactive Benchmark for Evaluating LLMs’ Sequential Reasoning Ability
by Siwei Yang, Bingchen Zhao, Cihang Xie
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts. The key feature is its interactive evaluation protocol, which requires LLMs to remember visited nodes and strategize subsequent moves. The authors build AQA-Bench with three different algorithms: binary search, depth-first search, and breadth-first search, and evaluate the sequential reasoning ability of 12 different LLMs. Notable findings include closed-source models showing strong sequential reasoning, open-source models struggling, and small models improving with a limited number of predecessor steps. The study highlights the complexity of LLM capabilities in sequential reasoning and aims to catalyze future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AQA-Bench is a new way to test how well language models can think ahead. It’s like a puzzle where you have to figure out what comes next. We tested 12 different models on three types of puzzles: finding something by looking for it in the right order, searching through things to find the first one that fits, and trying all options until we find the best one. The results showed that some models are really good at thinking ahead, but others need help or get confused. This study helps us understand how language models work and how we can make them better. |
