Papers with task complexity
Towards Annotating and Creating Summary Highlights at Sub-sentence Level (D19-54)
Copied to clipboard
| Challenge: | Creating summary highlights at the sub-sentence level is desirable because sub-entrances are more concise than whole sentences. |
| Approach: | They propose to annotate summary-worthy sub-sentences and teach classifiers to do the same . they frame task as selecting important sentences and identifying a single most informative textual unit from each sentence. |
| Outcome: | The proposed method reduces the complexity involved in sentence compression by reducing the number of words and phrases deleted. |
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts (2025.acl-long)
Copied to clipboard
Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si
| Challenge: | Large Language Models (LLMs) are capable of handling diverse tasks with well-crafted prompts and integration of external tools. |
| Approach: | They propose a prompt programming language that acts as a bridge between computer programs and LLMs by providing convenient conventions between them. |
| Outcome: | The proposed language is intuitive, concise, and efficient through representative scenarios including Chain-of-Thought with self-consistency (CoT-SC) and ReAct tool-use agent. |
From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing plans for large language model-based agents are limited by their granularity and lack flexibility. |
| Approach: | They propose a self-adaptive hierarchical planning mechanism that mimics human planning strategies and generates self-adapted hierarchic plans tailored to the varying difficulty levels of different tasks. |
| Outcome: | The proposed method significantly improves task execution success rates while mitigating overthinking at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks. |
Systematic Evaluation of Long-Context LLMs on Financial Concepts (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Long-context large language models (LC LLMs) are promising for tasks with long context windows . however, their ability to reliably utilize their growing context windows remains under investigation . |
| Approach: | They evaluate the performance of long-context large language models using a real-world financial news dataset. |
| Outcome: | The proposed models exhibit brittleness at longer context lengths even for simple tasks, the authors show . they advocate for more rigorous evaluation of LC LLMs by employing holistic metrics such as F1 (rather than recall) |
Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for automating data generation with Large Language Models (LLMs) are difficult, and we propose a semi-automatic approach to generate dialogs with attributions. |
| Approach: | They propose a semi-automatic approach to generate dialog queries and responses with Large Language Models followed by human verification and identification of attribution spans. |
| Outcome: | The proposed approach improves the quality of the response generation and attribution quality of MISeD datasets while reducing time and effort. |
Uncovering Stereotypes in Large Language Models: A Task Complexity-based Approach (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) have unlocked unprecedented applications of AI. |
| Approach: | They propose to use a social benchmark to evaluate the bias protection provided by Large Language Models (LLMs) with a variety of tasks with varying complexities to assess their effectiveness. |
| Outcome: | The proposed benchmark shows that both ChatGPT and GPT-4 have strong biases with respect to nationality, gender, race, and religion. |
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain (2020.acl-main)
Copied to clipboard
Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Marusczyk, Lukas Lange
| Challenge: | Using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network seems beneficial. |
| Approach: | They propose an annotation scheme for marking information on publications related to solid oxide fuel cells . they propose to use a recurrent neural network to solve a variety of tasks . |
| Outcome: | The proposed scheme is based on a corpus of 45 open-access scholarly articles and a neural network for a variety of tasks. |
Cognitive Biases, Task Complexity, and Result Intepretability in Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Recent work shows that cognitive biases occur frequently in language models . a cognitive bias is a systematic deviation in judgment that simplifies complex decisions . |
| Approach: | They evaluate the performance of different groups of models for each type of cognitive bias . they find that task complexity plays a part in eliciting stronger effects for some biases . |
| Outcome: | The proposed models perform better for each type of bias in different settings . the results show that task complexity plays a part in eliciting stronger effects . |
AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)
Copied to clipboard
Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin
| Challenge: | Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms. |
| Approach: | They propose a framework that leverages coordinated agents to manage memory across multiple granularities. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%. |
FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity. |
| Approach: | They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning. |
| Outcome: | The proposed method improves the performance of the end-to-end speech translation model on all 8 languages. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
Copied to clipboard
Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing multi-agent systems lack agent coordination and rely on predefined procedures . existing systems lack adaptive task coordination when task is big and complex . |
| Approach: | They propose a large-scale autonomous LLM-based multi-agent system that generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication and comprehensive system monitoring. |
| Outcome: | The proposed system outperforms existing systems in task completion efficiency and scalability. |
ADaPT: As-Needed Decomposition and Planning with Language Models (2024.findings-naacl)
Copied to clipboard
Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, Tushar Khot
| Challenge: | Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. |
| Approach: | They propose an approach that explicitly plans and decomposes complex sub-tasks when the LLM is unable to execute them. |
| Outcome: | The proposed approach significantly outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft. |
ThinkSwitcher: When to Think Hard, When to Think Fast (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large reasoning models excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. |
| Approach: | They propose a framework that enables a single LRM to dynamically switch between short and long CoT modes based on task complexity. |
| Outcome: | The proposed framework reduces computational cost by 20-30% while maintaining high accuracy on complex tasks. |
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for In-Context Learning (ICL) are sensitive to ICL-specific factors and rely on heuristic-based injection positions. |
| Approach: | They propose a method that extracts task-relevant representations from large language models and reinjects them during inference. |
| Outcome: | The proposed method outperforms few-shot In-Context Learning (ICL) and LoRA methods without repeated demonstration processing. |
Do You See Me : A Multidimensional Benchmark for Evaluating Visual Perception in Multimodal LLMs (2026.eacl-long)
Copied to clipboard
| Challenge: | Multimodal Large Language Models (MLLMs) show reasoning promise, yet their visual perception is a bottleneck. |
| Approach: | They propose a visual perception benchmark to test the visual perception of MLLMs. |
| Outcome: | The proposed benchmark examines MLLMs' visual perception abilities with 1758 images and 2612 questions. |
Active Learning with Non-Uniform Costs for African Natural Language Processing (2026.findings-eacl)
Copied to clipboard
| Challenge: | Annotating datasets for African languages is challenging due to the continent's vast linguistic diversity, complicating development of NLP systems. |
| Approach: | They propose a cost-aware active learning method that integrates BatchBALD acquisition strategy with a 0-1 Knapsack optimization objective to select informative and budget-efficient samples. |
| Outcome: | The proposed method outperforms BALD, BatchBALD, and stochastic sampling variants across cost scenarios on the MasakhaNEWS multilingual news classification benchmark covering 11 African languages. |
GAMBIT: A Gamified Jailbreak Framework for Multimodal Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing attacks focus on increasing the complexity of the modified visual task and do not explicitly leverage the model’s own reasoning incentives. |
| Approach: | They propose a framework that decomposes and reassembles harmful visual semantics and constructs a gamified scene that drives the model to explore, reconstruct intent and answer as part of winning the game. |
| Outcome: | Experiments on reasoning and non-reasoning MLLMs show that the proposed framework outperforms baseline models on both vision and text. |
PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers. |
| Approach: | They propose a multi-modal multi-document benchmark for agentic deep research that integrates evidence from multiple documents. |
| Outcome: | Experimental results show that even advanced systems achieve limited scores on PaperScope . paper provides a rigorous benchmark alongside a pipeline for constructing large multi-modal, multi-source deep research datasets. |
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)
Copied to clipboard
Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi
| Challenge: | Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities. |
| Approach: | They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS. |
| Outcome: | The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution . |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
Copied to clipboard
Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
DSG-MCTS: A Dynamic Strategy-Guided Monte Carlo Tree Search for Diversified Reasoning in Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown strong potential in complex reasoning tasks, but their performance often degrades, resulting in hallucinations, errors, and logical inconsistencies. |
| Approach: | They propose a framework that integrates multiple reasoning strategies to expand the reasoning space and a dynamic strategy selection mechanism that adapts to the task context. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on a set of reasoning benchmarks. |
Exploring and Predicting Transferability across NLP Tasks (2020.emnlp-main)
Copied to clipboard
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
| Challenge: | Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. |
| Approach: | They conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems. |
| Outcome: | The proposed model can improve performance even with low-data source tasks that differ substantially from the target task. |
Curse of Knowledge: Your Guidance and Provided Knowledge are biasing LLM Judges in Complex Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study has focused on simple settings, but their reliability in complex tasks remains understudied. |
| Approach: | They propose to use large language models as judges to evaluate reliability in complex tasks . they use a challenge benchmark to expose and quantify Auxiliary Information Induced Biases . |
| Outcome: | The proposed benchmark exposes and quantifies Auxiliary Information Induced Biases across 12 basic and 3 advanced scenarios. |
VisualEDU: A Benchmark for Assessing Coding and Visual Comprehension through Educational Problem-Solving Video Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . advanced proprietary models show promise, but struggle with increasing task complexity . |
| Approach: | VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to enhance output quality. |
| Outcome: | VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to improve output quality. |
Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing prompt optimization methods struggle with disjoint cases in complex tasks. |
| Approach: | They propose a tree-of-prompts structure which expands child prompts from parent prompts . they propose to use a nested if-else structure to address varying similarities and complexities . |
| Outcome: | The proposed tree-of-prompts outperforms PromptAgent and MoP on Gorilla, MATH and subset of BBH benchmarks. |
Out-of-Context Reasoning in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
| Approach: | They propose a lightweight technique that trains only new token embeddings on axioms . they train only new embeddables and evaluate them on unseen tasks . |
| Outcome: | The proposed technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks (2024.emnlp-main)
Copied to clipboard
| Challenge: | Evaluating generalisation capabilities of multimodal models based solely on performance on out-of-distribution data fails to capture their true robustness . proposed framework examines the role of instructions and inputs in generalisation abilities of such models, considering architectural design, input perturbations across language and vision modalities, and increased task complexity. |
| Approach: | They propose a framework that examines the role of instructions and inputs in the generalisation abilities of multimodal models, considering architectural design, input perturbations across language and vision modalities, and increased task complexity. |
| Outcome: | The proposed framework examines the role of instructions and inputs in the generalisation abilities of multimodal models, considering architectural design, input perturbations across language and vision modalities, and increased task complexity. |
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration (2026.findings-acl)
Copied to clipboard
Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen
| Challenge: | Existing benchmarks emphasize general-domain retrieval or static scientific question answering . SciExplore focuses on scientific database navigation, ambiguous literature retrieval, missing reference completion, and cross-source structured knowledge synthesis tasks. |
| Approach: | They propose a benchmark to evaluate scientific information-seeking and reasoning capabilities of LLMs and agents. |
| Outcome: | The new benchmark assesses the capabilities of state-of-the-art LLMs and agents in scientific research workflows. |
The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies show that LLM-based agents struggle to perform in zero-shot scenarios. |
| Approach: | They propose a framework to quantify the behavior gap between AI agents and human experts . they propose to examine discrepancies in dialog acts, tool usage, and knowledge utilization . |
| Outcome: | The proposed framework measures the behavior gap between AI agents and human experts on task-oriented dialogs. |
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains. |
| Approach: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models. |
| Outcome: | MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks . |
a1: Steep Test-time Scaling Law via Environment Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made remarkable advances in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during complex multi-step tasks. |
| Approach: | They propose a framework that enhances LLM reasoning through real-time environmental feedback validating each reasoning step, dynamic branch exploration for investigating alternative solution paths when faced with errors, and experience-based learning from successful reasoning trajectories. |
| Outcome: | The proposed model outperforms comparable models by 24.4 percentage points across benchmarks while outperforming comparable models. |
DR-Arena: an Automated Evaluation Framework for Deep Research Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for evaluating deep research capabilities rely on static datasets. |
| Approach: | They propose a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation. |
| Outcome: | DR-Arena achieves a Spearman correlation of 0.94 with the LMSYS Search Arena leaderboard. |
CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Entity matching (EM) requires fine-grained contextual understanding and domain knowledge. |
| Approach: | They propose a reinforcement learning controller that manages LLM operations by combining multiple operators and a set of model capacities. |
| Outcome: | The proposed controller can be reused with different LLM backends at inference time without retraining. |
OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand (2026.findings-acl)
Copied to clipboard
| Challenge: | Reasoning benchmarks are expensive to build and ill suited for isolating specific failure modes. |
| Approach: | They propose a framework and benchmark for diagnostic evaluation of legal reasoning that uses symbolic representations of U.S. Bankruptcy Code statutes to generate large space of reasoning tasks and their machine-computable solutions on demand. |
| Outcome: | The proposed framework and benchmark provides diagnostic insights into the competencies and failure modes of language models. |
MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026.findings-acl)
Copied to clipboard
Peng Wang, Yuxiong Yan, Xiao Ding, Kai Xiong, Bibo Cai, Chao Peng, Yutai Hou, Dandan Tu, Bing Qin, Ting Liu
| Challenge: | Existing causal datasets focus on the commonsense domain, but LLMs perform poorly when answering complex questions. |
| Approach: | They propose a multidisciplinary causal evaluation benchmark to assess LLMs' knowledge and skills. |
| Outcome: | The proposed model improves in domain specialization, structural diversity, and task complexity. |
TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks and datasets focus on single-agent settings, failing to capture the unique vulnerabilities of multi-agend LLM dynamics and co-ordination. |
| Approach: | They propose a benchmark to evaluate the robustness and safety of multi-agent LLM systems. |
| Outcome: | The proposed benchmark evaluates the robustness and safety of multi-agent LLM systems. |
What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Curriculum learning (CL) orders data corpus by difficulty, but prior work employs disparate difficulty metrics and training setups. |
| Approach: | They propose a framework that decomposes curriculum difficulty into five dimensions: Problem Difficulty, Model Surprisal, Confidence Margin, Predictive Uncertainty and Decision Variability. |
| Outcome: | The proposed framework decomposes curriculum difficulty into five dimensions . the results show that no curriculum strategy dominates universally . |
MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules (2026.findings-acl)
Copied to clipboard
| Challenge: | generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics. |
| Approach: | They propose a benchmark to evaluate and analyze the safety risks of molecular generation. |
| Outcome: | The proposed benchmark aims to evaluate and analyze the safety risks of molecular generation. |
Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models (2026.acl-long)
Copied to clipboard
| Challenge: | Recent Large Reasoning Models (LRMs) excel at complex reasoning tasks but often suffer from overthinking. |
| Approach: | They propose a two-stage fine-tuning strategy that progressively inspires LRMs’ difficulty cognition and redundancy cognition of LRM. |
| Outcome: | The proposed model significantly reduces inference costs by over 70% on easy tasks and 40% on complex ones without compromising performance. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
Copied to clipboard
Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
| Challenge: | Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems. |
| Approach: | They propose a hierarchical benchmark to evaluate large language models on engineering problems. |
| Outcome: | The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields. |