Papers with modular decomposition
LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)
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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Generating high-quality long-form survey articles poses significant challenges to AI Agent systems. |
| Approach: | They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines . |
| Outcome: | The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. |
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)
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| Challenge: | proprietary large language models (LLMs) have demonstrated impressive code generation performance. |
| Approach: | They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution. |
| Outcome: | The proposed framework outperforms baseline model and code generation methods on three popular benchmarks. |
A Simple, Yet Effective Approach to Finding Biases in Code Generation (2023.findings-acl)
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| Challenge: | Recent work shows that large language models can generate code on par with humans . however, data-driven approaches may not be sufficient for acquiring reasoning skills . |
| Approach: | They propose a framework that automatically identifies subtle cues a code generation model might exploit . they propose an automated intervention mechanism reminiscent of adversarial testing . |
| Outcome: | The proposed framework can be used as a data transformation technique during fine-tuning, acting as reversal strategy. |
PASTEL : Polarity-Aware Sentiment Triplet Extraction with LLM-as-a-Judge (2025.findings-acl)
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| Challenge: | Existing methods for extracting triplets of aspect terms and opinions are inadequate due to complexity of aspect-opinion interactions and implicit nature of sentiment dependencies in natural language. |
| Approach: | They propose a pipeline that decomposes the ASTE task into structured subtasks . they employ fine-tuned LLMs to separately extract the aspect and opinion terms . |
| Outcome: | The proposed pipeline outperforms existing baselines in the ASTE subtask. |