Papers with modular components
AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)
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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han
| Challenge: | Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. |
| Approach: | They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction. |
| Outcome: | The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. |
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI (2024.naacl-demo)
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Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman, Ofir Arviv, Matan Orbach, Shachar Don-Yehiya, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, Yoav Katz
| Challenge: | Textual data processing pipelines are tailored to specific datasets, task and model combinations. |
| Approach: | They propose a library for customizable textual data preparation and evaluation tailored to generative language models. |
| Outcome: | Unitxt is a library for customizable textual data preparation and evaluation tailored to generative language models. |
KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)
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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu
| Challenge: | Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) . |
| Approach: | They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). |
| Outcome: | The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. |
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language (2022.acl-long)
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| Challenge: | Currently, black-box models generate both the proof graph and intermediate inferences within the same model and thus may be unfaithful. |
| Approach: | They propose a transformer-based model that can perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. |
| Outcome: | The proposed model is robust to language perturbations and faster at inference than previous models on existing reasoning datasets. |
Learning Label Modular Prompts for Text Classification in the Wild (2022.emnlp-main)
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| Challenge: | Recent advances in parameter efficient tuning of pretrained language models have limited performance. |
| Approach: | They propose a label-modular prompt tuning framework for text classification tasks that emulates the transient nature of real-world. |
| Outcome: | The proposed framework outperforms baselines in two formidable settings and shows strong generalisation ability. |
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026.acl-long)
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| Challenge: | Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to perform codeflow by reusing existing functions over multiple turns. |
| Outcome: | The proposed benchmarks show that LLMs perform significantly worse in multi-turn codeflow scenarios and that their performance inversely correlates with dependency complexity. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis (2025.acl-long)
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Daniel Philip Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem, Kiril Gashteovski, Carolin Lawrence
| Challenge: | Existing approaches to DDx are limited by single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. |
| Approach: | They propose a Modular Explainable DDx Agent framework that allows physicians to iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. |
| Outcome: | The proposed framework achieves over 10% accuracy improvements in interactive DDx across large and small LLMs while offering critical explainability into its diagnostic reasoning process. |
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation (2024.lrec-main)
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| Challenge: | Existing approaches to modularity are limited to the case of pre-trained modules in a pre-training language model. |
| Approach: | They propose a method that allows the transfer of pre-trained PEFT modules between incompatible PLMs without any change in the inference complexity. |
| Outcome: | The proposed method allows the transfer of modules between incompatible PLMs without any change in the inference complexity. |