Summary of Evollama: Enhancing Llms’ Understanding Of Proteins Via Multimodal Structure and Sequence Representations, by Nuowei Liu et al.
EvoLlama: Enhancing LLMs’ Understanding of Proteins via Multimodal Structure and Sequence Representations
by Nuowei Liu, Changzhi Sun, Tao Ji, Junfeng Tian, Jianxin Tang, Yuanbin Wu, Man Lan
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed EvoLlama framework combines structure-based and sequence-based protein encoders with a Large Language Model (LLM) to enhance protein understanding. It consists of ProteinMPNN for structural information, ESM-2 for sequential knowledge, a multimodal projector to align representations, and Llama-3 as the text decoder. The model is trained on protein-oriented instructions and property prediction datasets verbalized via natural language instruction templates. Experimental results show that EvoLlama outperforms other fine-tuned protein-oriented LLMs in zero-shot settings by 1%-8% and surpasses state-of-the-art baselines with supervised fine-tuning by an average of 6%. The approach also achieves promising results on protein property prediction datasets, competitive with task-specific baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EvoLlama is a new way to understand proteins better. It combines different types of information about proteins to make predictions and answers questions. This helps scientists study proteins and figure out how they work. The model was tested and showed that it can do a good job, even without being trained specifically for certain tasks. This is important because it means EvoLlama can be used in many situations where we need to understand proteins. |
Keywords
» Artificial intelligence » Decoder » Fine tuning » Large language model » Llama » Supervised » Zero shot
