mlsanityData to 14 Oct 2026

Rethinking Text-based Protein Understanding: Retrieval or LLM?

arXiv 2505.20354 ↗First posted 26 May 2025cs.CL cs.AIcited by 4

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

In recent years, protein-text models have gained significant attention for their potential in protein generation and understanding. Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment, enabling simultaneous comprehension of textual descriptions and protein sequences. Through a thorough analysis of existing model architectures and text-based protein understanding benchmarks, we identify significant data leakage issues present in current benchmarks. Moreover, conventional metrics derived from natural language processing fail to accurately assess the model's performance in this domain. To address these limitations, we reorganize existing datasets and introduce a novel evaluation framework based on biological entities. Motivated by our observation, we propose a retrieval-enhanced method, which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. Our code and data can be seen at https://github.com/IDEA-XL/RAPM.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 24 of its 55 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

24 of 55 references.

References · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  5. 2024A Survey on LLM-as-a-Judgecited by 1,423
  6. 2022Galactica: A Large Language Model for Sciencecited by 744
  7. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  8. 2023MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adaptercited by 128
  9. 2023BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associationscited by 100
  10. 2023InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discoverycited by 86
  11. 2023ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Textscited by 78
  12. 2022PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understandingcited by 77
12 more
  1. 2022OntoProtein: Protein Pretraining With Gene Ontology Embeddingcited by 70
  2. 2023A Text-guided Protein Design Frameworkcited by 64
  3. 2024BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuningcited by 54
  4. 2024ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processingcited by 53
  5. 2023Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformerscited by 32
  6. 2024ProtT3: Protein-to-Text Generation for Text-based Protein Understandingcited by 25
  7. 2024ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Trainingcited by 21
  8. 2024A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understandingcited by 19
  9. 2024PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomescited by 14
  10. 2024EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representationscited by 9
  11. 2024Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Modelcited by 7
  12. 2024Unifying Sequences, Structures, and Descriptions for Any-to-Any Protein Generation with the Large Multimodal Model HelixProtXcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Predictioncited by 3
  2. 2026ProtRLSearch: A Multi-Round Multimodal Protein Search Agent with Large Language Models Trained via Reinforcement Learningcited by 1
  3. 2026Multimodal Mixture-of-Experts with Retrieval Augmentation for Protein Active Site Identificationcited by 1
  4. 2025Protein as a Second Language for LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
  2. 2026Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework
  3. 2023InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
  4. 2026Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
  5. 2026AMix-2: Establishing Protein as a Native Modality in Large Language Models
  6. 2024TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
  7. 2026BioBridge: Bridging Proteins and Language for Enhanced Biological Reasoning with LLMs
  8. 2025Lost in Tokenization: Context as the Key to Unlocking Biomolecular Understanding in Scientific LLMs