Papers with task decomposition
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature (D19-57)
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| Challenge: | Identifying and understanding the pathogenesis of genetic diseases is an essential task. |
| Approach: | They propose a joint deep learning model for gene mutation-disease knowledge extraction that adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition and relation extraction. |
| Outcome: | The proposed model achieves the average score of 0.45 on recognizing gene activities and disease entities and the average F1 score of 0.3 on extracting relations, ranking 1st in the AGAC RE task. |
AgentDiagnose: An Open Toolkit for Diagnosing LLM Agent Trajectories (2025.emnlp-demos)
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| Challenge: | Large Language Model (LLM) agents produce rich, multi-step trajectories that interleave observations, internal reasoning, and tool actions. |
| Approach: | They propose an open-source framework for diagnosing agent trajectories that quantifies five core agentic competencies and a visualization module that highlights trajectory semantics. |
| Outcome: | The proposed framework is extensible and compatible with most agent trajectories. |
CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)
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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang
| Challenge: | Existing knowledge base question answering methods struggle with complex queries. |
| Approach: | They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ. |
Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization (2026.eacl-industry)
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Kushal Chawla, Chenyang Zhu, Pengshan Cai, Sangwoo Cho, Scott Novotney, Ayushman Singh, Jonah Lewis, Keasha Safewright, Alfy Samuel, Erin Babinsky, Shi-Xiong Zhang, Sambit Sahu
| Challenge: | Summarization of multi-party dialogues is a critical capability in industry . but generating high-quality summaries in practice is challenging . prior work has focused on static datasets and benchmarks, a condition rare in practical scenarios . |
| Approach: | They present an agentic system to summarize multi-party interactions using static datasets. |
| Outcome: | The proposed system can summarize multi-party interactions using a set of complex requirements. |
ProConSuL: Project Context for Code Summarization with LLMs (2024.emnlp-industry)
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| Challenge: | Experimental results show that ProConSuL significantly improves code summaries and reduces the number of hallucinations. |
| Approach: | They propose a framework to provide a large language model with precise information about the code structure from program analysis methods. |
| Outcome: | The proposed framework significantly improves code summaries and reduces hallucinations compared to the base model. |
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection (2022.emnlp-main)
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Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab
| Challenge: | Hate speech detection is complex and requires commonsense reasoning and social nuance . prior work has shown that even humans cannot achieve a high agreement on whether a post constitutes HS . |
| Approach: | They frame a few-shot learning task to decompose a hate speech detection task into its "constituent" parts. they show that infusing commonsense knowledge from reasoning datasets improves the performance even further. |
| Outcome: | The proposed method outperforms baseline methods in the 16-shot case. |
On the Empirical Complexity of Reasoning and Planning in LLMs (2024.findings-emnlp)
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| Challenge: | Evidence shows that the relative performance of CoT, ToT, and their variants may vary from task to task. |
| Approach: | They propose to use chain-of-thought (CoT), tree-of thought (ToT), and related techniques to solve complex reasoning tasks with Large Language Models. |
| Outcome: | The proposed methods outperform the linear structure of CoT on hard reasoning tasks. |
Enhancing the General Agent Capabilities of Low-Paramter LLMs through Tuning and Multi-Branch Reasoning (2024.findings-naacl)
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| Challenge: | Open-source pre-trained Large Language Models exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks. |
| Approach: | They propose a method to construct agent-specific data using GPT-4 and supervised fine-tuning . they find that supervised tunning can significantly reduce hallucination outputs and formatting errors in agent tasks . |
| Outcome: | The proposed method improves on five agent tasks of AgentBench. |
Select-Then-Decompose: From Empirical Analysis to Adaptive Selection Strategy for Task Decomposition in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing task decomposition methods focus on memory, tool usage, and feedback mechanisms, but they often overlook the trade-off between performance and cost. |
| Approach: | They propose a strategy that selects the most suitable decomposition approach based on task characteristics and enhances the reliability of the results through a verification module. |
| Outcome: | The proposed strategy is based on categories of approaches, characteristics of tasks, and configuration of decomposition and execution models. |
TRANSIENTTABLES: Evaluating LLMs’ Reasoning on Temporally Evolving Semi-structured Tables (2025.naacl-long)
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| Challenge: | a recent study shows that large language models are limited in their ability to reason over time due to static datasets. |
| Approach: | They present a dataset that includes 3,971 questions derived from over 14,000 tables . they introduce a template-based question-generation pipeline that harnesses LLMs to refine questions . |
| Outcome: | The proposed model improves on the TRANSIENTTABLES dataset . it demonstrates that the model can reason over time, even when it is not static . |
Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies (2025.naacl-long)
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Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen
| Challenge: | Existing household robots are inability to recognize potential problems or dangers in home environments. |
| Approach: | They propose a task of creating anomaly scenarios using generative models instead of manually labeled data to build simulated environments. |
| Outcome: | The proposed framework outperforms existing models in terms of task description and scene diversity. |
Knowledge Editing through Chain-of-Thought (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods focus on multi-hop QA tasks and require frequent retraining. |
| Approach: | They propose a new knowledge editing framework that updates large language models with new information to maintain their world knowledge without retraining. |
| Outcome: | The proposed method achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods. |
A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check (2023.findings-emnlp)
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| Challenge: | Recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks. |
| Approach: | They propose to decompose Chinese Spelling Check into detection, reasoning, and searching subtasks and to train a module that is compatible with existing CSC models. |
| Outcome: | The proposed module can be trained for one model and benefit other models. |
RaDA: Retrieval-augmented Web Agent Planning with LLMs (2024.findings-acl)
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| Challenge: | Agents powered by large language models inherit important limitations such as the restricted context length, dependency on human-engineered exemplars, and insufficient generalization. |
| Approach: | They propose a novel planning method for Web agents that disentangles planning into two stages: for a new given task, it decomposes tasks into high-level subtasks; and then iteratively synthesizes actions based on dynamically retrieved exemplars. |
| Outcome: | The proposed method decomposes tasks into high-level subtasks and iteratively synthesizes actions based on dynamically retrieved exemplars. |
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
| Approach: | They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks. |
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)
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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
| Challenge: | Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints. |
| Approach: | They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs. |
| Outcome: | The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications. |
End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing multi-agent reinforcement learning methods depend on large critic networks to evaluate joint actions, leading to instability and high memory costs. |
| Approach: | They propose a method to optimize large language models for agent-specific roles . they propose combining agent-based frameworks with retrieval-augmented generation . |
| Outcome: | Experiments show that multi-agent group policy optimization outperforms baselines in task performance and computational efficiency. |
Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More (2025.acl-long)
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| Challenge: | Decoder-only LMs fail to solve the path-star task above 1/D chance due to a learned shortcut that absorbs training supervision. |
| Approach: | They propose a path-star task which is a minimal example of searching over a graph with D arms rooted at a single start node and a query to generate the arm with t from s to t. |
| Outcome: | The proposed task is solvable via decoder-only LMs and its minimal nature prevents its decomposition. |
Benchmarking Agentic Newswriting via Journalistic Workflows (2026.findings-acl)
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| Challenge: | Recent advances in autonomous digital agents highlight their potential for structured tasks through autonomous decision-making and task decomposition, but it remains unclear how well such systems support real-world information-intensive workflows. |
| Approach: | They propose a benchmark to evaluate how journalists can use agents to organize and organize information from the web. |
| Outcome: | The proposed system can be used to iterate and evaluate newswriting tasks in real-world situations. |