Challenge: BioT5+ is an extension of the BioT5, but lacked a nuanced understanding of molecular structures.
Approach: They propose a new bio-entity modeling framework, BioT5+, which integrates IUPAC names and molecule data.
Outcome: The proposed model bridges the gap between molecular representations and textual descriptions and improves the grounded reasoning of bio-text and bio-sequences.

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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations (2023.emnlp-main)

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Challenge: et al., 2022) argue that the current models for drug discovery lack the ability to integrate molecules, proteins, and natural language.
Approach: They propose a framework that integrates biological knowledge with chemical knowledge and natural language associations.
Outcome: The proposed framework shows superior performance across a wide range of tasks.
Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture (D19-57)

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Challenge: Using a pre-trained BERT-Base model, we learn domain-specific language representations using biomedical text.
Approach: They propose a system that extends BERT, a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus.
Outcome: The proposed model outperforms a baseline model while relying on an extremely simple setup with no specially engineered features.
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (D19-57)

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Challenge: BB system is among the top two systems in five of all six subtasks . knowledge about microbial diversity is crucial for the study of microbiome and bacteria .
Approach: They present a system that uses word embedding and lexical features to perform entities recognition, normalization and relation extraction.
Outcome: The proposed system achieves state-of-the-art in five of six subtasks and is among the top two in five.
Training Text-to-Molecule Models with Context-Aware Tokenization (2025.findings-emnlp)

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Challenge: Text-to-molecule models have shown great potential across chemical applications . however, they rely on atom-level tokenizations, which limiting the ability of models to capture global structural context within molecules.
Approach: They propose a text-to-molecule model that uses substructure-level tokenizations to model global connectivity.
Outcome: The proposed model outperforms state-of-the-art models using only 2% of training tokens.
CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023.emnlp-main)

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Challenge: Existing code LLMs adopt a specific architecture or rely on a unified encoder-decoder network for downstream tasks, lacking flexibility to operate in the optimal architecture for a particular task.
Approach: They propose to initialize code LLMs with frozen off-the-shelf LLM and explore instruction-tuning to align with natural language instructions.
Outcome: The proposed model outperforms open-source LLMs on 20 code-related benchmarks.
Rethinking Text-based Protein Understanding: Retrieval or LLM? (2025.emnlp-main)

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Challenge: Recent studies have focused on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment.
Approach: They 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.
Outcome: The proposed method significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models (2026.acl-long)

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Challenge: despite the success of large language models, their performance in highly specialized domains remains unsatisfactory.
Approach: They propose a biomedical tool-calling dataset designed for fine-tuning LLMs . the dataset contains 34 frequently used tools from the NCBI, Ensembl, and UniProt databases .
Outcome: The proposed dataset outperforms commercial LLMs on biomedical domains.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)

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Challenge: Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages.
Approach: They propose a unified pre-trained encoder-decoder Transformer model that leverages the code semantics conveyed from the developer-assigned identifiers.
Outcome: The proposed model outperforms existing models on understanding and generation tasks and can capture semantic information from code.
BioEL: A Comprehensive Python Package for Biomedical Entity Linking (2025.findings-naacl)

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Challenge: Entity Linking in biomedical literature is a critical task that enhances the extraction and integration of information from diverse scientific literature.
Approach: They propose a Python package that allows for better Entity Linking in biomedical literature . the package includes four components: Ontology Object, Dataset Object and Evaluation Framework .
Outcome: The proposed open-source package enables the implementation and comparison of biomedical entity linking tasks.
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)

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Challenge: Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences.
Approach: They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks .
Outcome: The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency.

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