NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear
Domain
- URL: http://arxiv.org/abs/2310.10920v1
- Date: Tue, 17 Oct 2023 01:27:20 GMT
- Title: NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear
Domain
- Authors: Anurag Acharya, Sai Munikoti, Aaron Hellinger, Sara Smith, Sridevi
Wagle, and Sameera Horawalavithana
- Abstract summary: NuclearQA is a human-made benchmark of 100 questions to evaluate language models in the nuclear domain.
We show how the mix of several types of questions makes our benchmark uniquely capable of evaluating models in the nuclear domain.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As LLMs have become increasingly popular, they have been used in almost every
field. But as the application for LLMs expands from generic fields to narrow,
focused science domains, there exists an ever-increasing gap in ways to
evaluate their efficacy in those fields. For the benchmarks that do exist, a
lot of them focus on questions that don't require proper understanding of the
subject in question. In this paper, we present NuclearQA, a human-made
benchmark of 100 questions to evaluate language models in the nuclear domain,
consisting of a varying collection of questions that have been specifically
designed by experts to test the abilities of language models. We detail our
approach and show how the mix of several types of questions makes our benchmark
uniquely capable of evaluating models in the nuclear domain. We also present
our own evaluation metric for assessing LLM's performances due to the
limitations of existing ones. Our experiments on state-of-the-art models
suggest that even the best LLMs perform less than satisfactorily on our
benchmark, demonstrating the scientific knowledge gap of existing LLMs.
Related papers
- Humanity's Last Exam [253.45228996132735]
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge.
It consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences.
Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval.
arXiv Detail & Related papers (2025-01-24T05:27:46Z) - One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks [68.33068005789116]
We present the first study aimed at objectively assessing the fairness and robustness of Large Language Models (LLMs) in handling dialects in canonical reasoning tasks.
We hire AAVE speakers, including experts with computer science backgrounds, to rewrite seven popular benchmarks, such as HumanEval and GSM8K.
Our findings reveal that textbfalmost all of these widely used models show significant brittleness and unfairness to queries in AAVE.
arXiv Detail & Related papers (2024-10-14T18:44:23Z) - MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs)
MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts.
It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
arXiv Detail & Related papers (2024-10-14T04:15:00Z) - Evaluating the Performance of Large Language Models via Debates [43.40134389150456]
Large Language Models (LLMs) are rapidly evolving and impacting various fields.
Most current approaches for performance evaluation are either based on fixed, domain-specific questions, or rely on human input.
We propose an automated benchmarking framework based on debates between LLMs, judged by another LLM.
This method assesses not only domain knowledge, but also skills such as argumentative reasoning and inconsistency recognition.
arXiv Detail & Related papers (2024-06-16T19:02:31Z) - The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models [94.31327813151208]
BiGGen Bench is a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks.
A key feature of the BiGGen Bench is its use of instance-specific evaluation criteria, closely mirroring the nuanced discernment of human evaluation.
arXiv Detail & Related papers (2024-06-09T12:30:30Z) - CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark.
In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship.
We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
arXiv Detail & Related papers (2023-11-29T08:29:54Z) - Generative Judge for Evaluating Alignment [84.09815387884753]
We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges.
Our model is trained on user queries and LLM-generated responses under massive real-world scenarios.
Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
arXiv Detail & Related papers (2023-10-09T07:27:15Z) - Through the Lens of Core Competency: Survey on Evaluation of Large
Language Models [27.271533306818732]
Large language model (LLM) has excellent performance and wide practical uses.
Existing evaluation tasks are difficult to keep up with the wide range of applications in real-world scenarios.
We summarize 4 core competencies of LLM, including reasoning, knowledge, reliability, and safety.
Under this competency architecture, similar tasks are combined to reflect corresponding ability, while new tasks can also be easily added into the system.
arXiv Detail & Related papers (2023-08-15T17:40:34Z) - CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity
and Infant Care [14.326936563564171]
We present a benchmark, CARE-MI, for evaluating misinformation in large language models (LLMs)
Our proposed benchmark fills the gap between the extensive usage of LLMs and the lack of datasets for assessing the misinformation generated by these models.
Using our benchmark, we conduct extensive experiments and found that current Chinese LLMs are far from perfect in the topic of maternity and infant care.
arXiv Detail & Related papers (2023-07-04T03:34:19Z) - Benchmarking Foundation Models with Language-Model-as-an-Examiner [47.345760054595246]
We propose a novel benchmarking framework, Language-Model-as-an-Examiner.
The LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner.
arXiv Detail & Related papers (2023-06-07T06:29:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.