mlsanityData to 14 Oct 2026

From Sentences to Sequences: Rethinking Languages in Biological System

arXiv 2507.00953 ↗First posted 1 Jul 2025q-bio.BM cs.AIcited by 3

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-regressive generation paradigm and evaluation metrics have been transferred from NLP to biological sequence modeling. However, the intrinsic structural correlations in natural and biological languages differ fundamentally. Therefore, we revisit the notion of language in biological systems to better understand how NLP successes can be effectively translated to biological domains. By treating the 3D structure of biomolecules as the semantic content of a sentence and accounting for the strong correlations between residues or bases, we highlight the importance of structural evaluation and demonstrate the applicability of the auto-regressive paradigm in biological language modeling. Code can be found at hrefhttps://github.com/zjuKeLiu/RiFoldgithub.com/zjuKeLiu/RiFold

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 6 of its 46 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

6 of 46 references.

References · 6

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  4. 2022AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBcited by 40
  5. 2018Learning to Design RNAcited by 29
  6. 2022Accurate RNA 3D structure prediction using a language model-based deep learning approachcited by 19

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generationcited by 129
  2. 2026RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusioncited by 3
  3. 2026RiboSphere: Learning Unified and Efficient Representations of RNA Structurescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Lost in Tokenization: Context as the Key to Unlocking Biomolecular Understanding in Scientific LLMs
  2. 2026The limits of bio-molecular modeling with large language models : a cross-scale evaluation
  3. 2026Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
  4. 2023InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
  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. 2023PoET: A generative model of protein families as sequences-of-sequences
  8. 2025Interpreting and Steering Protein Language Models through Sparse Autoencoders