ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
arXiv:2603.19515v1 Announce Type: new Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent studies have explored travel planning as a medium to integrate various verbal reasoning tasks into real-world contexts. However, reasoning tasks extend beyond verbal reasoning alone, and a comprehensive evaluation of LLMs requires a testbed that incorporates tasks from multiple cognitive domains. To address this gap, we introduce ItinBench, a benchmark that features one task of spatial reasoning, i.e., route optimization, into trip itinerary planning while keeping the traditional verbal reasoning tasks. ItinBench evaluates various LLMs across diverse tasks simultaneously, including Llama 3.1 8B, Mistral Large, Gemini 1.5 Pro, and GPT family. Our findings reveal that LLMs struggle to maintain
arXiv:2603.19515v1 Announce Type: new Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent studies have explored travel planning as a medium to integrate various verbal reasoning tasks into real-world contexts. However, reasoning tasks extend beyond verbal reasoning alone, and a comprehensive evaluation of LLMs requires a testbed that incorporates tasks from multiple cognitive domains. To address this gap, we introduce ItinBench, a benchmark that features one task of spatial reasoning, i.e., route optimization, into trip itinerary planning while keeping the traditional verbal reasoning tasks. ItinBench evaluates various LLMs across diverse tasks simultaneously, including Llama 3.1 8B, Mistral Large, Gemini 1.5 Pro, and GPT family. Our findings reveal that LLMs struggle to maintain high and consistent performance when concurrently handling multiple cognitive dimensions. By incorporating tasks from distinct human-level cognitive domains, ItinBench provides new insights into building more comprehensive reasoning testbeds that better reflect real-world challenges. The code and dataset: https://ethanwtl.github.io/IBweb/
Executive Summary
The article introduces ItinBench, a novel benchmark that evaluates large language models (LLMs) across multiple cognitive dimensions, including spatial reasoning and verbal reasoning. ItinBench features a trip itinerary planning task that incorporates route optimization, allowing for a comprehensive evaluation of LLMs' performance in handling diverse tasks simultaneously. The authors analyze the performance of four LLMs, highlighting their struggles to maintain high and consistent performance when handling multiple cognitive dimensions. The study provides valuable insights into the challenges of building comprehensive reasoning testbeds that reflect real-world challenges. The ItinBench benchmark and dataset are made available online, paving the way for further research and development in this field.
Key Points
- ▸ ItinBench is a novel benchmark that evaluates LLMs across multiple cognitive dimensions
- ▸ The benchmark features a trip itinerary planning task with route optimization
- ▸ LLMs struggle to maintain high and consistent performance when handling multiple cognitive dimensions
Merits
Comprehensive Evaluation
ItinBench provides a comprehensive evaluation of LLMs' performance across multiple cognitive dimensions, offering valuable insights into the challenges of building comprehensive reasoning testbeds.
Real-World Relevance
The ItinBench benchmark is designed to reflect real-world challenges, making it a valuable tool for evaluating LLMs' performance in practical applications.
Demerits
Limited Scope
The study focuses on a specific task (trip itinerary planning) and four LLMs, which may limit the generalizability of the findings to other tasks and models.
Methodological Limitations
The study does not provide a detailed description of the methodology used to design and implement the ItinBench benchmark, which may raise concerns about the validity and reliability of the results.
Expert Commentary
The introduction of ItinBench is a significant contribution to the field of LLM evaluation. By incorporating tasks from multiple cognitive dimensions, the benchmark provides a more comprehensive and realistic assessment of LLMs' performance. However, the study's limitations, such as the limited scope and methodological limitations, highlight the need for further research and development in this field. The implications of the study are far-reaching, with potential applications in various industries and policy domains. As the field of LLMs continues to evolve, the ItinBench benchmark will play a critical role in shaping the development of more sophisticated and capable AI systems.
Recommendations
- ✓ Future studies should focus on expanding the scope of the ItinBench benchmark to include a wider range of tasks and LLMs.
- ✓ Researchers should develop more detailed and transparent methodologies for designing and implementing LLM evaluation benchmarks.
Sources
Original: arXiv - cs.AI
