Papers with micro
SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation (2025.acl-industry)
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| Challenge: | Recommender systems are a key component of our day-to-day lives, but evaluation remains a challenge due to the gap between offline metrics and online behaviors. |
| Approach: | They propose a framework that enables users to build believable human proxies from historical data. |
| Outcome: | The proposed framework exhibits closer alignment with real humans than previous work, both at micro and macro levels. |
CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation (2025.emnlp-industry)
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| Challenge: | Existing models rely on rigid, hand-crafted rules to model nuanced behavior in urban environments. |
| Approach: | They propose an urban simulator that generates realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. |
| Outcome: | The proposed urban simulator exhibits closer alignment with real humans than previous work. |
Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks (2026.eacl-industry)
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Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita, Koichi Nagatsuka, Hikaru Tomonari, Yasuhiro Sogawa
| Challenge: | Domain-adaptive pre-training (DAPT) is one approach for enabling LLMs to handle unseen knowledge. |
| Approach: | They propose to disentangle the answering process into three subtasks and evaluate the performance of each subtask. |
| Outcome: | The proposed model resolves the elicitation task that the base model struggled with but does not resolve other subtasks. |
AraDIC: Arabic Document Classification Using Image-Based Character Embeddings and Class-Balanced Loss (2020.acl-srw)
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| Challenge: | a novel image-based character embedding framework is used for text classification in Arabic . classical methods require morphological analysis, word segmentation, and hand-crafted feature engineering. |
| Approach: | They propose a novel end-to-end Arabic document classification framework, Arabic document image-based classifier, inspired by image-basic character embeddings. |
| Outcome: | The proposed framework improves on modern standard Arabic, colloquial Arabic, and Classical Arabic. |
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
| Outcome: | The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus. |
Automatic Section Recognition in Obituaries (2020.lrec-1)
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| Challenge: | Obituaries contain information about people’s values across times and cultures, which makes them useful for exploring cultural history. |
| Approach: | They propose to use a convolutional neural network to recognize these sections in obituaries to improve their annotation. |
| Outcome: | The proposed model outperforms bag-of-words and embedding-based BiLSTMs and BiLStm-CRFs with a micro F1 = 0.81. |
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding (2025.findings-emnlp)
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| Challenge: | EmoGist is a training-free, in-context learning method for visual emotion classification . context-dependent definitions of emotion labels could allow more accurate predictions of emotions . |
| Approach: | They introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. |
| Outcome: | The proposed method improves micro F1 scores and macro F1 with LVLMs. |
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
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| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
Applications of BERT Models Towards Automation of Clinical Coding in Icelandic (2024.findings-naacl)
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| Challenge: | Traditionally, clinical coding is manual and laborintensive task prone to human error. |
| Approach: | They analyze 25 years of electronic health records from the Landspitali University Hospital in Icelandic to explore the potential of using NLP for clinical coding. |
| Outcome: | The best-performing model achieves competitive results in micro and macro F1 scores, with label attention contributing significantly to its success. |
Chinese Discourse Parsing: Model and Evaluation (2020.lrec-1)
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| Challenge: | Chinese discourse parsing has not yet a consistent evaluation metric . micro vs. macro F1 scores, binary v. multiway ground truth, and left-heavy v . right-heaviness binarization are important for Chinese discourses . |
| Approach: | They propose a neural network model that unifies a pre-trained transformer and a CKY-like algorithm and compare it with previous models with different evaluation scenarios. |
| Outcome: | The proposed model outperforms the previous models with different evaluation scenarios. |
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization (N18-1)
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| Challenge: | a proposed model for generating natural language descriptions is too generic and does not exploit task specific characteristics. |
| Approach: | They propose a model which uses a fused bifocal attention mechanism to exploit micro and macro level information and a gated orthogonalization mechanism to ensure that a field is remembered for a few time steps and then forgotten. |
| Outcome: | The proposed model improves on a recently released dataset with two similar datasets for French and German. |
MCDTB: A Macro-level Chinese Discourse TreeBank (C18-1)
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| Challenge: | Discourse analysis is becoming increasingly important in the field of natural language processing. |
| Approach: | They propose to annotate macro discourse information and additional discourse information to make annotation more objective and accurate. |
| Outcome: | The results show that the annotations are more objective and accurate than the previous ones. |
Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)
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| Challenge: | Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated. |
| Approach: | They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction. |
| Outcome: | The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results. |
Prompt-Based Metric Learning for Few-Shot NER (2023.findings-acl)
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| Challenge: | Existing metric learning methods do not fully incorporate label semantics into modeling. |
| Approach: | They propose a method to largely improve metric learning for few-shot named entity recognition (NER) a pre-defined category is a key natural language understanding task . |
| Outcome: | The proposed method outperforms the previous state-of-the-art (SOTA) method with 16 of 18 settings outperformed previous methods by 9.12% and 34.51% . |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
| Approach: | They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently . |
| Outcome: | The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width. |
New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT? (2020.coling-main)
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| Challenge: | Existing methods for fine-grained event classification are of tiny size, ranging from 5-10K events. |
| Approach: | They propose to use ACLED data for fine-grained event classification . they compare performance of various state-of-the-art models on these datasets . |
| Outcome: | The proposed models perform better on micro (94.3-94.9%) and macro F1 (86.0-88.9%) the proposed models are robust and the performance is dependent on training data size. |
Cooking Up a Neural-based Model for Recipe Classification (2020.lrec-1)
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Elham Mohammadi, Nada Naji, Louis Marceau, Marc Queudot, Eric Charton, Leila Kosseim, Marie-Jean Meurs
| Challenge: | a dataset of cooking recipes in French is highly imbalanced due to collaborative nature of the dataset . authors propose a neural-based model to address the first task of the DEFT 2013 shared task . |
| Approach: | They propose a neural-based model to address the first task of the DEFT 2013 shared task . they use state-of-the-art embedding approaches and deep architectures to address imbalanced dataset . |
| Outcome: | The proposed model outperforms models that use only pretrained embeddings in micro and macro F1 scores. |
AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation (2026.acl-long)
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| Challenge: | Existing evaluation practices for recommender systems rely on few-shot prompting and offline metrics are often misaligned with online behavior. |
| Approach: | They propose a framework that learns world-model-driven agents from human interactions. |
| Outcome: | The proposed framework enables agents to express rich preferences and feedback in natural language and interact with recommender systems in a simulation. |