Mol-Instructions Multi-choice
Models answer biomedical multiple-choice questions. The score is test-set accuracy.
One track of Mol-Instructions. The Mol-Instructions record keeps lab scores that combine tracks.
- Made by
- Zhejiang University (ZJUNLP)
- Released
- June 2023
- Kind
- Genomics and life sciences
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Short answer
- Tasks
- All public
In the authors’ words
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability.
Mol-Instructions (LLaMA3-instruct) tops the leaderboard with 96.1%
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
- 1 Mol-Instructions (LLaMA3-instruct) 96.1%
- 2 Mol-Instructions (LLaMA2-chat) 64.9%
- 3 PMC_LLaMa 62.5%
- 4 Galactica 31.2%
- 5 LLaMa 29.7%
- 6 Vicuna 29%
- 7 Alpaca 28.6%
- 8 Baize 23.7%
- 9 ChatGLM 22.3%
Source: Official Mol-Instructions Multi-choice leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Mol-Instructions (LLaMA3-instruct) | 96.1% | Official leaderboard | ||
| 2 | Mol-Instructions (LLaMA2-chat) | 64.9% | Official leaderboard | ||
| 3 | PMC_LLaMa | 62.5% | Official leaderboard | ||
| 4 | Galactica | 31.2% | Official leaderboard | ||
| 5 | LLaMa | 29.7% | Official leaderboard | ||
| 6 | Vicuna | 29% | Official leaderboard | ||
| 7 | Alpaca | 28.6% | Official leaderboard | ||
| 8 | Baize | 23.7% | Official leaderboard | ||
| 9 | ChatGLM | 22.3% | Official leaderboard |
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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