Summary of Evaluating Llm Reasoning in the Operations Research Domain with Orqa, by Mahdi Mostajabdaveh et al.
Evaluating LLM Reasoning in the Operations Research Domain with ORQA
by Mahdi Mostajabdaveh, Timothy T. Yu, Samarendra Chandan Bindu Dash, Rindranirina Ramamonjison, Jabo Serge Byusa, Giuseppe Carenini, Zirui Zhou, Yong Zhang
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Operations Research Question Answering (ORQA), a new benchmark that assesses the generalization capabilities of Large Language Models (LLMs) in the domain of Operations Research (OR). ORQA evaluates whether LLMs can emulate OR experts’ knowledge and reasoning skills when confronted with complex optimization problems. The dataset, developed by OR experts, features real-world optimization problems that require multistep reasoning to construct mathematical models. Evaluations of open-source LLMs like LLaMA 3.1, DeepSeek, and Mixtral reveal modest performance, highlighting a gap in their ability to generalize to specialized technical domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new test for big language models that tries to figure out how well they can understand and solve complex math problems in the field of operations research. The dataset was made by experts in this field and includes real-world problems that need multiple steps to solve. The results show that these language models are not very good at solving these kinds of problems, which is interesting because it shows that they might not be as smart as we think. |
Keywords
» Artificial intelligence » Generalization » Llama » Optimization » Question answering
