# SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading

arXiv: https://arxiv.org/abs/2406.10421 · HTML: https://mlsanity.com/paper/2406.10421/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://mlsanity.com/paper/2105.03011/) (2021)
- [MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark](https://mlsanity.com/paper/2402.04788/) (2024)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge](https://mlsanity.com/paper/2102.03315/) (2021)

## Built on by (most cited first, 14 of 14)

- [AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model for Atmospheric Science](https://mlsanity.com/paper/2502.01159/) (2025)
- [Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probability](https://mlsanity.com/paper/2502.11115/) (2025)
- [Benchmarks Are Not Atomic: Composition-Aware LLM Evaluation using BenchHub](https://mlsanity.com/paper/2506.00482/) (2025)
- [SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs](https://mlsanity.com/paper/2603.20253/) (2026)
- [Testing Low-Resource Language Support in LLMs Using Language Proficiency Exams: the Case of Luxembourgish](https://mlsanity.com/paper/2504.01667/) (2025)
- [Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons](https://mlsanity.com/paper/2506.03785/) (2025)
- [PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A](https://mlsanity.com/paper/2602.21045/) (2026)
- [An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems](https://mlsanity.com/paper/2602.21059/) (2026)
- [Challenges and Recommendations for LLM-as-a-Judge in Multilingual Settings and for Low-Resource Languages](https://mlsanity.com/paper/2607.02235/) (2026)
- [Mechanisms of Matter: Language Inferential Benchmark on Physicochemical Hypothesis in Materials Synthesis](https://mlsanity.com/paper/2509.25281/) (2025)
- [From Slides to Chatbots: Enhancing Large Language Models with University Course Materials](https://mlsanity.com/paper/2510.22272/) (2025)
- [CLINB: A Climate Intelligence Benchmark for Foundational Models](https://mlsanity.com/paper/2511.11597/) (2025)
- [S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services](https://mlsanity.com/paper/2602.14017/) (2026)
- [Evaluating RAG Metrics in Applied Contexts: An Experiment, Its Findings and Its Limitations](https://mlsanity.com/paper/2607.07302/) (2026)

## Abstract (arXiv)

With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is performing tasks on scientific topics, such as writing algorithms, querying databases or giving mathematical proofs. Inspired by the way university students are evaluated on such tasks, in this paper, we propose SciEx - a benchmark consisting of university computer science exam questions, to evaluate LLMs ability on solving scientific tasks. SciEx is (1) multilingual, containing both English and German exams, and (2) multi-modal, containing questions that involve images, and (3) contains various types of freeform questions with different difficulty levels, due to the nature of university exams. We evaluate the performance of various state-of-the-art LLMs on our new benchmark. Since SciEx questions are freeform, it is not straightforward to evaluate LLM performance. Therefore, we provide human expert grading of the LLM outputs on SciEx. We show that the free-form exams in SciEx remain challenging for the current LLMs, where the best LLM only achieves 59.4% exam grade on average. We also provide detailed comparisons between LLM performance and student performance on SciEx. To enable future evaluation of new LLMs, we propose using LLM-as-a-judge to grade the LLM answers on SciEx. Our experiments show that, although they do not perform perfectly on solving the exams, LLMs are decent as graders, achieving 0.948 Pearson correlation with expert grading.