Papers with Low-Resource NLP
ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026.tacl-1)
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| Challenge: | Active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. |
| Approach: | They propose an active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. |
| Outcome: | The proposed approach outperforms existing methods ADAPET, PERFECT, and SetFit in few-shot scenarios and can be extended to non-few scenarios. |
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation (2024.findings-acl)
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| Challenge: | Existing methods for data augmentation generate new examples wildly without proper control, which hinders the usefulness of the proposed approach. |
| Approach: | They propose a chain-of-thought attribute manipulation approach that generates new data from existing examples by tweaking in the user-provided attribute. |
| Outcome: | The proposed approach generates new data from existing examples by tweaking in the user-provided, task-specific attribute, e.g., sentiment polarity or topic in movie reviews. |
Computational modeling of semantic change (2024.eacl-tutorials)
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| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
| Outcome: | In historical linguistics, tools and methods have been developed to analyse the process . they include categorisations of types of change, causes and mechanisms . but traditional methods, while informative, are often based on small, carefully curated samples. |
Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)
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| Challenge: | Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese. |
| Approach: | They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes . |
| Outcome: | The proposed method outperforms existing benchmarks on the Chinese lexical substitution task. |
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations (2022.naacl-demo)
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| Challenge: | Information Extraction (IE) analysts use supervised machine learning to define the schema and build a training corpus with annotated examples. |
| Approach: | They propose a workflow where the analyst verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE. |
| Outcome: | The proposed workflow performs very well on four IE tasks with a single user interface and a video demonstration is available on vimeo. |
Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization (2024.emnlp-industry)
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| Challenge: | Existing approaches to generate concise summarizations require extensive modifications to the architecture. |
| Approach: | They propose a lightweight model that can be finetuned to extract salient keyphrases from the source document to enhance ROUGE F1 and recall. |
| Outcome: | The proposed model can be finetuned to extract salient keyphrases without any LLM customization. |
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting (2021.naacl-industry)
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| Challenge: | a challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. |
| Approach: | They propose a method for training retrieval-based dialogue systems using annotated data and a larger, unlabeled dataset. |
| Outcome: | The proposed method improves model performance offline and online compared with no pretraining . the model is deployed in an agent-support application and evaluated on live customer service contacts . |
Learning with Limited Text Data (2022.acl-tutorials)
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| Challenge: | Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP . |
| Approach: | This tutorial will provide a systematic overview of methods for learning from limited labeled data. |
| Outcome: | This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions . |
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022.findings-acl)
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| Challenge: | Existing approaches to extract relation triplets require large datasets and a fixed set of relations. |
| Approach: | They propose to use a sentence-based task setting to generalize relation extraction methods to unseen relation sets. |
| Outcome: | The proposed method can extract multiple relation triplets in a sentence using language model prompts and structured text approaches. |
Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning (2024.naacl-short)
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Ge Bai, Chenji Lu, Daichi Guo, Shilong Li, Ying Liu, Zhang Zhang, Guanting Dong, Ruifang Liu, Sun Yong
| Challenge: | Existing approaches to few-shot Relation Extraction (RE) are prone to confusion when applying knowledge to a target domain with entirely new types of relations. |
| Approach: | They propose a relation-aware prompt learning method with pre-training to clear confusion by decomposing relation types through an innovative label prompt. |
| Outcome: | The proposed method outperforms previous sota methods and yields better results on cross-domain few-shot RE tasks. |
Semantic Specialization of Distributional Word Vectors (D19-2)
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| Challenge: | Distributional word vectors conflate various paradigmatic and syntagmatic lexico-semantic relations. |
| Approach: | This tutorial provides an overview of specialization methods for distributional word vectors . a common solution is to include external lexico-semantic knowledge in a reshaped vector space . |
| Outcome: | This paper provides an overview of specialization methods for distributional word vectors . the most recent developments include a new method for asymmetric relations in Euclidean . |
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
| Approach: | They propose to use emotional patterns identified by clinical practitioners to enhance the prediction capabilities of a mental illness detection model built using a deep neural network architecture. |
| Outcome: | The proposed method achieves a task-specific AUC higher than 0.90 . it compares multi-task learning with multi-channel convolutional neural network and multiple inputs to methods such as multi-class classification . |
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction (2022.naacl-demo)
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Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
| Challenge: | Vacareanu et al., 2021) proposes a system that helps users build transparent information extraction models . rule-based methods address the opacity of neural architectures by producing models that are transparent . |
| Approach: | They propose a system that assists a user in constructing transparent information extraction models . the system generates high-precision rules even in a 1-shot setting, they show . |
| Outcome: | The proposed system generates high-precision rules even in a 1-shot setting . it outperforms manually written patterns on a widely-used relation extraction dataset . |
InstructGEC: Enhancing Unsupervised Grammatical Error Correction with Instruction Tuning (2025.coling-main)
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| Challenge: | Recent studies have proposed methods of generating synthetic data for unsupervised GEC . however, the cost of such methods is high and the quality of the data is poor . |
| Approach: | They propose a method to generate synthetic data automatically for unsupervised GEC . they use a masking strategy to mask an erroneous sentence and the instruction consistently . |
| Outcome: | The proposed method outperforms state-of-the-art unsupervised methods on English and Chinese GEC datasets. |
PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing (2022.emnlp-main)
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| Challenge: | Existing approaches to provide constructive feedback to counselors are limited by the time and cost involved. |
| Approach: | They propose a system that takes as input a client prompt and a counselor response and outputs a score indicating the level of reflection in the counselor response. |
| Outcome: | The proposed model outperforms baselines on different metrics and can be used to provide useful feedback to counseling trainees. |
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
| Approach: | They propose a zero-shot transfer learning technique where training data are synthesized from an abstract dialogue model and the ontology of the domain. |
| Outcome: | The proposed technique improves the state of the art on the multi-domain dialogue state tracking dataset by 21%. |
Active Learning for New Domains in Natural Language Understanding (N19-2)
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| Challenge: | Existing approaches to improve the accuracy of new domains are lacking annotated live utterances. |
| Approach: | They propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances and a sequence labeling model to prioritize informative examples. |
| Outcome: | The proposed algorithm achieves 6.6%-9% error rate reduction and statistically significant improvements on six new domains. |
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. |
Z3D: Zero-Shot 3D Visual Grounding from Images (2026.acl-short)
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Nikita Drozdov, Andrey Lemeshko, Nikita Gavrilov, Anton Konushin, Danila Rukhovich, Maksim Kolodiazhnyi
| Challenge: | 3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. |
| Approach: | They propose a zero-shot 3D visual grounding pipeline that operates on multi-view images without geometric supervision and without object priors. |
| Outcome: | Experiments on ScanRefer and Nr3D show that the proposed method outperforms existing methods. |
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)
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| Challenge: | Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers. |
| Approach: | They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees. |
| Outcome: | The proposed method improves on speech conversation and math problems. |
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis (2020.aacl-srw)
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| Challenge: | Automated Essay Scoring (AES) is a process that aims to alleviate the workload of graders and improve the feedback cycle in educational systems. |
| Approach: | They propose to combine two tasks, sentiment analysis and AES by utilizing multi-task learning to combine sentiment features extracted from opinion expressions. |
| Outcome: | The proposed model produces a QWK of 0.763 on the Automated StudentAssessment Prize (ASAP) benchmark. |
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation (2024.eacl-demo)
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| Challenge: | Recent advances in text-to-image diffusion models have made it difficult to obtain high-quality images. |
| Approach: | They propose an adaptive framework that automatically enhances a user's prompt to improve the quality of generation models. |
| Outcome: | The proposed framework generates prompts similar to those produced by human prompt engineers and provides user control over stylistic features via constraint set specification. |
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from related Example Banks (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive few-shot learning capabilities through in-context learning. |
| Approach: | They propose a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. |
| Outcome: | The proposed approach outperforms existing frameworks for retrieving examples on low-resource Indic languages. |
Improving Parallel Sentence Mining for Low-Resource and Endangered Languages (2025.acl-short)
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| Challenge: | Parallel sentence mining is a technique used to find matching sentence pairs from a source and target language. |
| Approach: | They propose a benchmark dataset for parallel sentence mining on three low-resource languages . they apply alignment post-processing and cluster-based isotropy enhancement techniques to one of them . |
| Outcome: | The proposed datasets show better mining quality overall for low-resource languages . the proposed methods are crucial for optimizing parallel data extraction for low resource languages - a new study shows. |
FastFit: Fast and Effective Few-Shot Text Classification with a Multitude of Classes (2024.naacl-demo)
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| Challenge: | Few-shot prompting of large language models (LLMs) via API calls presents a unique challenge when dealing with a multitude of classes that share similar semantic meanings. |
| Approach: | They present a Python package that integrates batch contrastive learning and token-level similarity score to provide fast few-shot classification. |
| Outcome: | The proposed method significantly improves multi-class classification speed and accuracy across English and Multilingual datasets. |
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages (2022.acl-long)
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| Challenge: | Pre-trained multilingual models have shown great potential for zero-shot cross-lingual transfer to low web-resource languages (LRLs). |
| Approach: | They propose a vocabulary generation algorithm which enhances lexical overlap across related languages by generating a token that increases the representation of LRLs. |
| Outcome: | The proposed approach improves cross-lingual transfer accuracy without reducing HRL representation and accuracy. |
APLenty: annotation tool for creating high-quality datasets using active and proactive learning (D18-2)
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| Challenge: | APLenty is an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. |
| Approach: | They present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. |
| Outcome: | The proposed tool is highly flexible and can be adapted to various other tasks. |
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation (D19-3)
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| Challenge: | 80% of biomedical data is stored in unstructured text such as electronic health records (EHRs). |
| Approach: | They propose a web-based interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. |
| Outcome: | The proposed interface is designed to build, improve and customise a NER+L model for biomedical domain text and collate accurate research use case specific training data. |
Discourse-Aware In-Context Learning for Temporal Expression Normalization (2024.naacl-short)
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| Challenge: | Temporal expression (TE) normalization is a well-studied problem, but upcoming machine learning approaches suffer from a lack of labeled data. |
| Approach: | They propose to use in-context learning to inject task, document, and example information into a large language model for temporal expression normalization. |
| Outcome: | The proposed model performs better in non-standard settings by dynamically including relevant examples during inference. |
ProPy: Building Interactive Prompt Pyramids upon CLIP for Partially Relevant Video Retrieval (2025.findings-emnlp)
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| Challenge: | Existing models for PRVR use unimodal features, but powerful pretrained vision-language models like CLIP are underexplored. |
| Approach: | ProPy is a model with systematic architectural adaptation of CLIP specifically designed for PRVR. |
| Outcome: | ProPy outperforms existing models on three public datasets in terms of performance on the datasets. |
Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning (2020.aacl-main)
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| Challenge: | Existing methods to generate text from KB triples are limited and expensive . a novel approach is proposed to train the generation model in unsupervised way . |
| Approach: | They propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples. |
| Outcome: | The proposed method outperforms existing methods and is cost-effective. |
Discovering the Functions of Language in Online Forums (D19-55)
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| Challenge: | a vast amount of work has been dedicated to speech act categorization for characterizing discourses . lack of formalism and diversity of taxonomies make it difficult to compare different annotated datasets. |
| Approach: | They propose a semi-supervised framework for predicting the functions of Reddit comments . they propose to use the framework to analyze online forum conversations . |
| Outcome: | The proposed framework can predict functions of Reddit comments and 165K comments. |
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes (2021.naacl-industry)
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| Challenge: | Existing methods for training numeric attributes are based on manual labeling and distant supervision leads to incomplete training annotations. |
| Approach: | They propose a multi-task learning architecture to deal with missing attribute values in training data, removing dependency on manual annotations. |
| Outcome: | The proposed framework improves on 20 numeric attributes extracted from 5 product categories and 3 english marketplaces with language-agnostic performance. |
Combining Weakly Supervised ML Techniques for Low-Resource NLU (2021.naacl-industry)
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| Challenge: | Recent advances in transfer learning have improved the performance of virtual assistants . however, meager training data is often a key bottleneck in creating voice-enabled applications . |
| Approach: | They propose to use unsupervised and semi-supervised techniques to improve NLU accuracy . they incorporate anonymized, unlabeled and automatically transcribed user utterances into training . |
| Outcome: | The proposed methods improve NLU accuracy in low-resource settings by integrating unsupervised and SSL techniques. |
Familarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data (2025.naacl-long)
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| Challenge: | Current research relies on large synthetic datasets to train zero-shot named entity recognition models. |
| Approach: | They propose a metric that captures the semantic similarity between entity types in training and evaluation to estimate label shift. |
| Outcome: | The proposed metric captures semantic similarity between entity types in training and evaluation, and their frequency in training data to provide an estimate of label shift. |
Structure and Label Constrained Data Augmentation for Cross-domain Few-shot NER (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) tasks require large datasets with accurate annotations that are labor-intensive and time-consuming. |
| Approach: | They propose a method to leverage domain gaps to model cross-domain few-shot named entity recognition (NER) NER is a natural language processing task to detect entity mentions and classify them into predefined labels . |
| Outcome: | The proposed method achieves state-of-the-art or competitive results on standard datasets. |
skweak: Weak Supervision Made Easy for NLP (2021.acl-demo)
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| Challenge: | skweak is a Python-based toolkit for NLP developers to use weak supervision . labelled data remains a scarce resource in many practical NLP scenarios . |
| Approach: | They present a Python-based toolkit that allows NLP developers to use weak supervision . skweak is designed to facilitate the use of weak supervision for NLP tasks . |
| Outcome: | skweak is a Python-based toolkit that facilitates weak supervision . the toolkit provides a simple interface to apply labels to a large corpus of text data . |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (N18-1)
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| Challenge: | Existing unsupervised methods for learning hypernyms from unlabeled text are not scaled to large vocabularies or yield unacceptably poor accuracy. |
| Approach: | They propose an unsupervised method of hypernym discovery using word contexts . they use word2vec to embed word context distributions without supervision . |
| Outcome: | The proposed method provides double the precision and highest average performance on 11 datasets. |
Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection (2024.acl-long)
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| Challenge: | Stance detection is used to infer attitudes from human communications . stance decisions involve complex judgments generated by LLMs . |
| Approach: | They propose a method for stance detection which relies on a new prompting framework . it allows for more than one stance object type and no examples of stance attribution . |
| Outcome: | The proposed method outperforms fine-tuned stance detection systems. |
Unsupervised Neologism Normalization Using Embedding Space Mapping (D19-55)
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| Challenge: | Neologisms refer to recent expressions that are specific to certain entities or events, but have not yet been accepted into mainstream language. |
| Approach: | They propose an unsupervised approach for detecting and normalizing neologisms in social media content without relying on parallel training data. |
| Outcome: | The proposed method detects neologisms and normalizes them to canonical words without training data. |
DropMix: A Textual Data Augmentation Combining Dropout with Mixup (2022.emnlp-main)
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| Challenge: | Existing methods to overcome overfitting in text learning do not consider dimensionality . dimensionalization is important for deep neural networks to overcome the problem . |
| Approach: | They propose a saliency map-based approach to overcome overfitting in text learning . they propose augmentation regularization methods such as Dropout and Mixup to improve regularization . |
| Outcome: | Empirical results show that the proposed approach overcomes overfitting in text learning . dropout and mixup methods are effective in enhancing regularization . |
How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages? (2024.acl-short)
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| Challenge: | a recent study focused on machine translation evaluation for low-resource languages . linguistic aspects that vary across languages are factors that will exacerbate the problem in low-source languages due to the reliance on extensive data resources. |
| Approach: | They propose to use multi-dimensional quality metrics and DA annotations to meta-evaluate MT evaluation metrics for low-resource languages. |
| Outcome: | The proposed evaluation metrics are based on human scores on the candidate translations of assamese, maithili, and Punjabi. |
Scalable Prompt Generation for Semi-supervised Learning with Language Models (2023.findings-eacl)
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| Challenge: | Prompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding datasets and tasks. |
| Approach: | They propose to use a set of prompt tokens to create diverse prompt models and a varying number of soft prompt token to encourage language models to learn different prompts. |
| Outcome: | The proposed method achieves the best average accuracy of 71.5% in different few-shot learning settings. |
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization (2025.emnlp-demos)
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| Challenge: | PromptSculptor automates the iterative prompt optimization process for Text-to-Image models . previous work focused on generating detailed, high-quality prompts based on user feedback . |
| Approach: | They propose a framework that decomposes a task into four specialized agents . they use Chain-of-Thought reasoning to transform a short, vague user prompt into a comprehensive, refined prompt. |
| Outcome: | The proposed framework significantly improves output quality and reduces iterations needed for user satisfaction. |
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024.acl-short)
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| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
| Approach: | They propose a guidance data augmentation technique that abstracts context and sentence structure and maintains context-entity relationships for DA. |
| Outcome: | The proposed method enhances the training performance of named entity recognition tasks while maintaining context-entity relationships. |
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching (2023.findings-emnlp)
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| Challenge: | Existing methods to learn informative entity embeddings are insufficient for semi-supervised entity alignment. |
| Approach: | They propose a semi-supervised method which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings. |
| Outcome: | The proposed method is superior to existing methods on benchmark datasets and further analyses. |
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing studies on weak supervision for NLU focus on a specific task or simulate weak supervision signals from ground-truth labels. |
| Approach: | They propose a benchmark to advocate and facilitate research on weak supervision for NLU . they use document-level and token-level prediction tasks as examples . |
| Outcome: | The proposed benchmark advocates and facilitates research on weak supervision for NLU tasks. |
Self-Training with Weak Supervision (2021.naacl-main)
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| Challenge: | State-of-the-art deep neural networks require large amounts of labeled training data that is expensive to obtain or not available for many tasks. |
| Approach: | They propose a weak supervision framework that leverages all available data for a given task . they leverage task-specific unlabeled data through self-training with a model that predicts pseudo-labels for instances that may not be covered by weak rules . |
| Outcome: | The proposed framework improves on state-of-the-art datasets on six benchmark tasks. |
Enhancing LLM-as-a-Judge through Active-Sampling-based Prompt Optimization (2025.acl-industry)
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| Challenge: | Suboptimal prompts can introduce biases, inconsistencies, and unreliable evaluations. |
| Approach: | They propose an active-sampling-based framework for automatic prompt optimization . they use a small, diverse subset of samples to guide prompt refinement . |
| Outcome: | The proposed framework outperforms baselines on four popular LLMs and three real-world datasets. |
BEnQA: A Question Answering Benchmark for Bengali and English (2024.findings-acl)
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| Challenge: | a dataset of parallel Bengali and English exam questions is used to compare LLMs in low-resource languages. |
| Approach: | They introduce BEnQA, a dataset comprising parallel Bengali and English exam questions . they benchmark several Large Language Models with their parallel dataset and observe performance disparity . |
| Outcome: | The proposed dataset consists of 5K questions covering several subjects in science . the authors find that the models perform poorly in Bengali and English . |
Modeling Temporality of Human Intentions by Domain Adaptation (D18-1)
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| Challenge: | Recent research shows that themes and words within a conversation change across time, whereas topics and the patient's attitude towards their willingness to change might shift. |
| Approach: | They propose a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). |
| Outcome: | The proposed method improves on a college alcoholism dataset using a bi-LSTM and topic model to learn language usage change across different time sessions. |
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training (2023.eacl-main)
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| Challenge: | Obtaining high-quality human labelled data is an expensive and noisy process. |
| Approach: | They propose to leverage unlabelled data to improve the sample efficiency of the models. |
| Outcome: | The proposed methods can be used to extract the Cause-Effect relation between a given head entity and tail entity based on context in the input sentence. |
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering (2023.acl-long)
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| Challenge: | In-context learning is a common practice to randomly sample examples to serve as context. |
| Approach: | They propose a new principle for in-context learning that helps each sample find an in-constitut example organization that can derive the correct prediction. |
| Outcome: | The proposed method achieves 40% relative improvement over the common practice setting. |
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Task-adaptive pre-training (TAPT) and Self-training can be complementary with simple TFS protocol. |
| Approach: | They propose to use task-adaptive pre-training and self-training to combine TAPT and ST with a simple TFS protocol to achieve strong combined gains across six datasets. |
| Outcome: | The proposed method can achieve strong combined gains across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. |
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)
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| Challenge: | Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data. |
| Approach: | They propose two methods to effectively utilize large language models for low-resource text summarization. |
| Outcome: | The proposed methods synthesize high-quality documents using LLaMA-3-70b-Instruct model . they achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data. |
Multi-Source Attention for Unsupervised Domain Adaptation (2020.aacl-main)
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| Challenge: | Existing approaches for domain adaptation (UDA) focus on adapting to a domain from a single source domain, but labelled instances are not available for the target domain. |
| Approach: | They propose to model source-selection in unsupervised domain adaptation as an attention-learning problem, where attention is learned over the sources per given target instance. |
| Outcome: | The proposed method outperforms previous proposed methods on two cross-domain sentiment classification datasets and is able to explain the predictions. |
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation (2021.eacl-main)
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| Challenge: | Recent advances in neural retrieval have led to advancements on document, passage and knowledge-base benchmarks. |
| Approach: | They propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. |
| Outcome: | The proposed approach can exceed term-based techniques on document retrieval benchmarks by using domain-targeted synthetic question generation. |
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (2021.emnlp-main)
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| Challenge: | Relation extraction systems require large amounts of labeled examples which are costly to annotate. |
| Approach: | They propose to use hand-made relation extraction tasks to refine a pretrained textual entailment engine which is run as-is or further fine-tuned on labeled examples. |
| Outcome: | The proposed system achieves 63% F1 zero-shot, 69% with 16 examples per relation and 4 points short of the state-of-the-art system on the same conditions. |
Emotion Cause Extraction on Social Media without Human Annotation (2023.findings-acl)
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| Challenge: | Existing studies have focused on extracting emotion causes from news articles, but lack of fine-grained annotations has limited the ECE task. |
| Approach: | They propose a new ECE framework that extracts emotion causes from social media data without relying on human annotations. |
| Outcome: | The proposed framework achieves high extraction performance and generalizability without relying on human annotations. |
Self-Supervised Test-Time Learning for Reading Comprehension (2021.naacl-main)
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| Challenge: | Recent work on unsupervised question answering shows that models can be trained with procedurally generated question-answer pairs and achieve performance competitive with supervised methods. |
| Approach: | They propose a method that performs "test-time learning" on a given context . they use self-supervision to train models on synthetically generated question-answer pairs . |
| Outcome: | The proposed method outperforms current unsupervised methods and outperformed supervised methods. |
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation (2021.acl-long)
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| Challenge: | Large-scale conversational systems typically generate unnatural, robotic responses using template-based approaches. |
| Approach: | They propose a data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model to automatically create MR-to-Text data from open-domain texts. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. |
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability (2021.acl-long)
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| Challenge: | In-domain experts are recruited to reannotate augmented samples and determine to what extent each strategy preserves the original rating. |
| Approach: | They implement 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires. |
| Outcome: | The data augmentation strategies outperform task-agnostic augmentations and automatic augmentation systems perform worst on the MIND-CA corpus. |
Leveraging Only the Category Name for Aspect Detection through Prompt-based Constrained Clustering (2022.findings-emnlp)
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| Challenge: | Aspect category detection (ACD) aims to automatically identify user-concerned aspects from online reviews. |
| Approach: | They propose a method that relies on the category name of each aspect and a pretrained language model to generate constraints for clustering. |
| Outcome: | The proposed framework performs better than existing weakly supervised methods on nine benchmark datasets. |
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning (2020.coling-main)
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| Challenge: | Concept prerequisite chain learning is an unsupervised task with no access to labeled concept pairs during training. |
| Approach: | They propose a model that uses deep learning representations to predict concept relations . they frame concept prerequisite chain learning as an unsupervised task with no labeled concept pairs . |
| Outcome: | The proposed model outperforms semi-supervised methods in terms of accuracy and F1 score. |
What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study (2023.findings-emnlp)
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| Challenge: | Using a few-shot prompt, we examine the effects of symbols and patterns on in-context learning in large language models. |
| Approach: | They employ a counterfactual prompting approach by manipulating examples and testing the consequences on model behavior. |
| Outcome: | The proposed approach allows us to understand the relative contributions of symbols and patterns on in-context learning. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
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| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)
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| Challenge: | Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization. |
| Approach: | They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks. |
| Outcome: | The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks. |
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
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| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
GuideTree: Guideline-Induced Review Trees for Long Medical Records (2026.acl-industry)
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| Challenge: | Medical record reviewers must produce consistent, traceable, guideline-compliant outcomes . longcontext inference is expensive and often degrades as inputs grow . |
| Approach: | a new method compiles textual guidelines into a fixed review tree . a cost-aware split-and-prune search is used to update the tree offline . the algorithm produces consistent, traceable, guideline-compliant outcomes . |
| Outcome: | The proposed system outperforms the strongest non-expert baselines by 84.5–92.8 Macro-F1 . it reduces average I/O volume to 74K input+output characters and average latency to 22s . |
CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. |
| Approach: | They propose a collaborative multi-agent, multi-reasoning-path prompting framework that prompts LLMs to play different roles in a problem-solving team and encourages different role-play agents to collaboratively solve the target task. |
| Outcome: | The proposed framework is applied to two college-level science problems over competitive baselines. |
CAST: Achieving Stable LLM-based Text Analysis for Data Analytics (2026.findings-acl)
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| Challenge: | Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labeling. |
| Approach: | They propose a framework that enhances output stability by constraining the model’s latent reasoning trajectory. |
| Outcome: | The proposed framework improves stability by constraining the model's latent reasoning trajectory. |
The TechQA Dataset (2020.acl-main)
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Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang
| Challenge: | TECHQA is a domain-adaptation question answering dataset for the technical support domain. |
| Approach: | They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum . |
| Outcome: | The TECHQA dataset highlights two real-world issues from the automated customer support domain. |
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols (2021.naacl-main)
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| Challenge: | Recent work shows that probabilistic context-free grammars with neural parameterization can be effective in unsupervised constituency parsing. |
| Approach: | They propose a parameterization form of PCFGs based on tensor decomposition which has at most quadratic computational complexity in the symbol number. |
| Outcome: | The proposed model improves unsupervised constituency parsing performance across ten languages. |
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)
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| Challenge: | Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles. |
| Approach: | They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control. |
| Outcome: | Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability. |
Similarity-Based Content Scoring - A more Classroom-Suitable Alternative to Instance-Based Scoring? (2023.findings-acl)
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| Challenge: | Recent work suggests that similarity-based content scoring methods can yield comparable results to instance-based supervised learning. |
| Approach: | They propose to use similarity-based scoring to achieve similar results . they compare different instance-based and similarity based methods on multiple data sets . |
| Outcome: | The proposed approach has a lower need for annotated training data and better zero-shot performance, but the results are not consistent with previous studies. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
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| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |
BART-TL: Weakly-Supervised Topic Label Generation (2021.eacl-main)
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| Challenge: | Existing methods for topic labeling use weak labelers to train rankers . recent studies show that weakly-supervised methods can produce meaningful labels . |
| Approach: | They propose a weakly-supervised method for assigning topic labels to models by using weak labelers. |
| Outcome: | The proposed model can generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks. |
EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)
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| Challenge: | Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes. |
| Approach: | They propose to use a model prompting technique to prompt models of event implications by targeting their understanding of physical attributes. |
| Outcome: | The proposed model prompting technique is especially useful for unseen attributes or when only limited data is available. |
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (D19-1)
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| Challenge: | a lack of research on multilingual or cross-lingual task-oriented dialog systems has limited results . we propose a zero-shot adaptation of task-orientated dialog systems to low-resource languages . task-focused systems are often trained with monolingual datasets that are expensive to build or acquire . |
| Approach: | They propose a zero-shot adaptation of multilingual task-oriented dialog systems to low-resource languages using latent variables and a set of very few parallel word pairs. |
| Outcome: | The proposed model performs better in natural language understanding task compared to state-of-the-art model . the proposed model uses very few parallel word pairs to refine cross-lingual representations . |
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations (2023.acl-long)
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| Challenge: | Existing methods for zero-shot learning are based on in-context training, but performance drops when no demonstrations are available. |
| Approach: | They propose a new method that constructs pseudo-demonstrations for a given test input using a raw text corpus and applies techniques to reduce copying. |
| Outcome: | The proposed method outperforms previous zero-shot methods on nine classification datasets and is on par with in-context learning with labeled training data in the few-shot setting. |
Plum: Prompt Learning using Metaheuristics (2024.findings-acl)
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Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, KaShun Shum, Jipeng Zhang, Renjie Pi, Tong Zhang
| Challenge: | Recent advances in prompt learning have led to a need for general prompt optimization methods. |
| Approach: | They propose a branch of discrete non-convex optimization methods with over 100 options as a promising approach to prompt learning. |
| Outcome: | The proposed methods can be used to discover more human-understandable prompts that were previously unknown in reasoning and image generation tasks. |
Event-Guided Denoising for Multilingual Relation Learning (2020.coling-main)
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| Challenge: | Existing methods for general purpose relation extraction use a fixed set of predetermined relations, but research has shifted to the identification of unseen relations in any language. |
| Approach: | They propose a method for collecting high quality relation training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. |
| Outcome: | The proposed method achieves comparable results to the current state-of-the-art when trained on a smaller multilingual encoder . |
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2024.findings-emnlp)
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| Challenge: | Existing studies on retrieval-augmented generation (RAG) rarely address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect. |
| Approach: | They propose a framework that induces RAG models to alter latent factors and analyzes the effect on their answers. |
| Outcome: | The proposed framework identifies two critical factors affecting RAG models' confidence in their answers and analyzes the effect on their answers. |
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification (2024.findings-naacl)
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| Challenge: | Existing research has focused on fully supervised XMC, but real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. |
| Approach: | They propose a framework that generates a set of candidate labels through in-context learning and then reranks them. |
| Outcome: | The proposed framework advances state-of-the-art on two diverse public benchmarks. |
Advancing Reasoning with Off-the-Shelf LLMs: A Semantic Structure Perspective (2025.findings-emnlp)
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| Challenge: | Existing reasoning models suffer from hallucinations and unfaithfulness, whereas general LLMs perform suboptimal on complex tasks. |
| Approach: | They propose a structure analysis method that helps LLMs better understand the question structure and guide the problem-solving process. |
| Outcome: | The proposed method improves zero-shot performance on knowledge-intensive and mathematical tasks while demonstrating strong robustness against corrupted reasoning paths. |
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
| Approach: | They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. |
| Outcome: | The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively. |
A Coarse-to-Fine Prototype Learning Approach for Multi-Label Few-Shot Intent Detection (2024.findings-emnlp)
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| Challenge: | Existing methods for few-shot intent detection are limited due to data scarcity and lack of information for unseen domains. |
| Approach: | They propose to enhance utterance representations with label synset augmentation and refine prototypes by distilling coarse domain knowledge from a universal teacher model. |
| Outcome: | The proposed approach outperforms existing methods in terms of accuracy and generalization across domains. |
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (2022.emnlp-main)
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| Challenge: | a new method for textual style transfer is proposed for text with a limited set of style choices . textual styles are a complex task that requires specialized models to perform . |
| Approach: | They propose a method for arbitrary textual style transfer using pre-trained language models . they use a mathematical formulation of the TST task, decomposing it into three components . |
| Outcome: | The proposed method performs on par with state-of-the-art large-scale models while using less compute and memory. |
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
| Outcome: | The proposed method performs better than existing supervised systems using word embeddings. |
A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing approaches to few-shot named entity recognition require large amounts of labeled data. |
| Approach: | They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures . |
| Outcome: | The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset. |
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation (2022.emnlp-main)
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| Challenge: | Question answering models often suffer from performance deterioration upon deployment . |
| Approach: | They propose a self-supervised framework called QADA for QA domain adaptation . they propose to augment training QA samples with hidden space augmentation . |
| Outcome: | The proposed framework improves on multiple target datasets over state-of-the-art methods. |
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (2023.acl-long)
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| Challenge: | Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. |
| Approach: | They propose a plan-and-solve (PS) prompting that includes a few manual steps to generate reasoning steps and improves the quality of generated reasoning steps. |
| Outcome: | The proposed strategy outperforms Zero-shot-CoT on ten reasoning problems and has comparable performance to 8-shot CoT prompting on the math reasoning problem. |
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)
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Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
| Challenge: | Existing prompt transfer techniques lack consideration for dialogue-specific information. |
| Approach: | They propose a method which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task. |
| Outcome: | The proposed method significantly outperforms baselines on two dialogue summarization benchmarks. |
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions (2024.naacl-long)
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Siddhant Arora, Hayato Futami, Jee-weon Jung, Yifan Peng, Roshan Sharma, Yosuke Kashiwagi, Emiru Tsunoo, Karen Livescu, Shinji Watanabe
| Challenge: | Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model’s behavior and surpassing performance of task-specific models. |
| Approach: | They adapt a pre-trained automatic speech recognition model to additional tasks using single-token task specifiers. |
| Outcome: | The proposed model can generalize to new datasets and languages for seen task types. |
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing approaches to few-shot relation extraction require training. |
| Approach: | They propose a method for few-shot relation extraction using large language models, called CoT-ER, chain-of-thought with explicit evidence reasoning. |
| Outcome: | The proposed approach achieves competitive performance compared to the fully-supervised state-of-the-art approach on the FewRel1.0 and FewRela2.0 datasets. |
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters (2023.acl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). |
| Approach: | They propose to use Chain-of-Thought (CoT) prompting to encourage the LLM to generate intermediate rationales for solving a problem by providing a series of reasoning steps in the demonstrations. |
| Outcome: | The proposed model can generate coherent lines of reasoning even with invalid demonstrations while still generating coherent lines during inference. |
Automatic Multiple-Choice Question Generation and Evaluation Systems Based on LLM: A Study Case With University Resolutions (2025.coling-main)
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Sérgio Silva Mucciaccia, Thiago Meireles Paixão, Filipe Wall Mutz, Claudine Santos Badue, Alberto Ferreira de Souza, Thiago Oliveira-Santos
| Challenge: | Multiple choice questions (MCQs) are often used in employee selection and training, but their creation is resource-intensive and requires significant effort and investment. |
| Approach: | They propose to use large language models and prompt engineering techniques to automate the generation and validation of MCQs. |
| Outcome: | The proposed system reduces the burden on human resources and enables scalable, cost-effective MCQ generation. |
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (2022.emnlp-main)
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Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, Taeuk Kim
| Challenge: | Intuitively, ground-truth labels should have as much impact in in-context learning as supervised learning, but the impact of the quality of demonstrations remains elusive. |
| Approach: | They propose to measure input-label correspondence and ground-truth label effect ratio . they propose to use verbosity of prompt templates and language model size as controlling factors . |
| Outcome: | The proposed metrics show that ground-truth labels have less impact than previously thought . the authors identify key components as controlling factors to achieve noise-resilient ICL . |
Mitigating Exposure Bias in Grammatical Error Correction with Data Augmentation and Reweighting (2023.eacl-main)
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| Challenge: | Existing approaches to grammatical error correction (GEC) use sequence-to-sequence models, but there is an exposure bias problem. |
| Approach: | They propose a data manipulation approach to overcome the exposure bias problem in seq2seq GEC . they propose augmentation methods to mimic decoder input and reweighting methods to automatically balance the importance of each kind of augmented samples. |
| Outcome: | The proposed method improves on benchmark GEC datasets. |
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)
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| Challenge: | Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. |
| Approach: | They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. |
| Outcome: | The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities. |
Learning a Policy for Opportunistic Active Learning (D18-1)
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| Challenge: | Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in interactive object retrieval tasks. |
| Approach: | They propose to use active learning to constrain possible queries during interactions to improve grounding of natural language descriptions in an interactive object retrieval task. |
| Outcome: | The proposed policy trades off task completion with model improvement that would benefit future tasks while lowering the cost of annotation without sacrificing model performance. |
From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning (2025.naacl-long)
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| Challenge: | Motivated by in-context learning capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities when multiple image-text pairs are provided as demonstrations. |
| Approach: | They conduct systematic and principled evaluation of multimodal ICL for models of different scales on a broad spectrum of new yet critical tasks. |
| Outcome: | The proposed model performance improves on a broad spectrum of new yet critical tasks. |
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
Self-Renewal Prompt Optimizing with Implicit Reasoning (2024.findings-emnlp)
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Zihan Liang, Ben Chen, Zhuoran Ran, Zihan Wang, Huangyu Dai, Yufei Ma, Dehong Gao, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in NLP have been driven by the development of Large Language Models (LLMs). |
| Approach: | They propose a self-renewal approach to optimize LLM outputs to better align with human preferences without supervised fine-tuning. |
| Outcome: | The proposed approach improves outputs to better align with human preferences across LLMs and tasks without supervised fine-tuning. |
Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)
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| Challenge: | Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive. |
| Approach: | They propose to query a masked language model with cloze style prompts to obtain supervision signals. |
| Outcome: | The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%. |
An Invariant Learning Characterization of Controlled Text Generation (2023.acl-long)
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| Challenge: | Controlled generation is a problem of creating text that contains stylistic or semantic attributes of interest. |
| Approach: | They propose a distribution shift-based control system that can be used to train a predictor of the desired attribute. |
| Outcome: | The proposed method shows that the most effective predictor should be invariant across multiple text environments. |
Unsupervised Mapping of Arguments of Deverbal Nouns to Their Corresponding Verbal Labels (2023.findings-acl)
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| Challenge: | Deverbal nouns are nominal forms of verbs used in English texts to describe events or actions . many NLP systems neglect to handle nominalized constructions, resulting in limited applications . |
| Approach: | They propose to map arguments of deverbal nouns to universal-dependency relations of verbal constructions . they propose to use the same labels as verbal cases to map the arguments . |
| Outcome: | The proposed approach maps arguments of nominalized nouns to the corresponding verbal constructions. |
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation. |
| Approach: | They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data. |
| Outcome: | The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
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| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024.acl-long)
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| Challenge: | Large language models (LLMs) are known to perform tasks by simply observing few exemplars, but performance among under-represented languages falls behind due to pre-training data imbalance. |
| Approach: | They propose to assemble synthetic exemplars from high-resource languages to prompt LLMs to translate from any language into English and use them to create intra-lingual exemplar models to perform tasks in target languages. |
| Outcome: | The proposed method outperforms supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages. |
Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using CRF Autoencoders (2020.findings-emnlp)
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| Challenge: | Existing work on cross-lingual adaptation of dependency parsers without annotated target corpora focuses on discriminative source parser ignoring unannotated corporata . |
| Approach: | They propose to use unsupervised discriminative parsers to adapt dependency parser to unannotated target corpora without a supervised generative parsing method. |
| Outcome: | The proposed method significantly outperforms previous methods. |
In-Context Learning for Few-Shot Dialogue State Tracking (2022.findings-emnlp)
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| Challenge: | Existing methods for zero-shot and few-shot learning dialogue state tracking are hard and expensive. |
| Approach: | They propose an in-context learning framework for zero-shot and few-shot learning dialogue state tracking (DST) a large pretrained language model takes a test instance and a few exemplars as input and directly decodes the dialogue state . |
| Outcome: | The proposed framework outperforms state-of-the-art models in few-shot settings . it is flexible and scalable, and requires less data to adapt to new domains and scenarios . |
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues (2023.findings-eacl)
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| Challenge: | Discourse processing suffers from data sparsity, especially for dialogues . a variety of discourse frameworks have been proposed to extract discourse information from dialogues. |
| Approach: | They propose unsupervised and semi-supervised methods to infer latent discourse structures for dialogues based on attention matrices from Pre-trained Language Models. |
| Outcome: | The proposed methods achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and semi-supervised methods, respectively. |
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (2020.acl-main)
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| Challenge: | Xie et al., 2016) demonstrate that semi-supervised learning models suffer from over-fitting when there is only limited labeled data. |
| Approach: | They propose a semi-supervised learning method for text classification using a data augmentation method called TMix. |
| Outcome: | The proposed method outperforms pre-trained and fine-tuned models on several text classification benchmarks. |
A Multi-Task Learning Framework for Multi-Target Stance Detection (2021.findings-acl)
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| Challenge: | Existing models fail to learn target-specific representations and are prone to overfitting. |
| Approach: | They propose a multi-task learning network to train one model on all target pairs . their results show that their proposed model outperforms the best-performing baseline by 12.39% . |
| Outcome: | The proposed model outperforms the best-performing baseline model by 12.39% in macro-averaged F1-score. |
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)
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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
| Challenge: | Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios. |
| Approach: | They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers . |
| Outcome: | The proposed model outperforms baselines and class transfer models in low-resource scenarios. |
A Probabilistic Framework for Discovering New Intents (2023.acl-long)
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| Challenge: | Existing methods for detecting unknown intents do not explore the intrinsic structure of unlabeled data. |
| Approach: | They propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. |
| Outcome: | The proposed framework can be used to discover intents with latent variables . it can be applied to three challenging real-world datasets . |
Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning (2023.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction can only recognize predefined relation types . new or out-of-scope relation types may continually emerge after the model is deployed . |
| Approach: | They propose a novel relation detection task that uses self-supervised learning to handle shallow semantic similarity problem. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two datasets. |
On-the-fly Definition Augmentation of LLMs for Biomedical NER (2024.naacl-long)
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| Challenge: | Despite their general capabilities, LLMs struggle on biomedicalNER tasks due to specialized terminology and lack of training data. |
| Approach: | They propose a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly. |
| Outcome: | The proposed approach improves performance on biomedicalNER tasks by 15% (on average) The proposed method outperforms fine-tuned language models in few-shot settings. |
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods to extract relation facts from limited labeled corpora are laborintensive to obtain . Existing approaches use self-training to generate pseudo labels that will cause gradual drift problem or leverage meta-learning scheme which does not solicit feedback explicitly. |
| Approach: | They propose a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. |
| Outcome: | The proposed method handles two major scenarios in low-resource relation extraction when no unlabeled data is available. |
Semi-Supervised Sequence Modeling with Cross-View Training (D18-1)
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| Challenge: | Unsupervised representation learning algorithms such as word2vec and ELMo only learn from task-specific labeled data during the main training phase. |
| Approach: | They propose a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
| Outcome: | The proposed algorithm improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning (2024.naacl-long)
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| Challenge: | Recent advances of Large Language Models (LLMs) have been pushing the field of Natural Language Processing (NLP) to the next level in many different aspects. |
| Approach: | They propose a novel labeling method which estimates how much incremental knowledge is brought into LLMs by a demonstration. |
| Outcome: | The proposed method estimates how much incremental knowledge is brought into the LLMs by a demonstration. |
XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples (2025.findings-naacl)
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| Challenge: | XAMPLER: Cross-Lingual Example Retrieval is a cross-lingual example retrieval method . large language models (LLMs) have emerged as effective in-context learning methods . |
| Approach: | They propose a method to train a multilingual model with annotated English examples . they use annotized English data to train the model and use it to train other languages . |
| Outcome: | XAMPLER: Cross-Lingual Example Retrieval improves in-context learning in English . it trains a retriever based on a multilingual small language model using annotated English examples . |
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation (2024.lrec-main)
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| Challenge: | Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. |
| Approach: | They propose a method which explicitly models MDG’s multi-step reasoning process and iteratively enhances this reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art methods across objective and subjective evaluations on two publicly available datasets. |
Weakly-Supervised Questions for Zero-Shot Relation Extraction (2023.eacl-main)
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| Challenge: | Zero-Shot Relation Extraction (ZRE) is a task where the training and test sets have no shared relation types. |
| Approach: | They propose to learn a model that can translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. |
| Outcome: | The proposed model outperforms the state-of-the-art on the fewrel and WikiZSL datasets by more than 16 F1 points without using gold question templates. |
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval (2022.findings-acl)
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| Challenge: | a new model for parallel sentence retrieval can be used to align parallel sentences in multilingual corpora . a faithful aligner can help narrow down the candidate pool without having to deal with an enormous search space . |
| Approach: | They propose a model that can be trained on only one language pair and transfers to low-resource languages with negligible degradation in performance. |
| Outcome: | The proposed model outperforms the previous model on the Tateoba dataset by 8.0 points in accuracy and using less than 0.6% of their parallel data. |
Evaluation of Lifelong Learning Systems (2020.lrec-1)
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| Challenge: | Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis. |
| Approach: | They propose a generic evaluation methodology for lifelong learning systems . they use "initialisation data" to refer to the set of training, development and test data together . |
| Outcome: | The proposed evaluation method is based on the evaluation of human-assisted learning outside the context of lifelong learning. |
ReCoT-NER: Enhancing Zero-Shot Named Entity Recognition through Chain-of-Thought Prompting and Recall-Oriented Loss Optimization (2026.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a key component of natural language processing (NLP) but it is difficult to implement in specialized domains such as wind power fault diagnosis. |
| Approach: | They propose a reasoning-enhanced generative framework that integrates Chain-of-Thought prompting and recall-oriented loss optimization to address these challenges. |
| Outcome: | The proposed framework improves recall and overall F1 performance across general and industrial domains. |
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning paradigm. |
| Approach: | They propose a framework that incorporates prior predictions and feedback to improve sentiment understanding by incorporating prior feedback and leveraging a feedback-driven prompt. |
| Outcome: | The proposed framework improves on nine sentiment analysis datasets with an average improvement of 5.95% over conventional methods. |
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods for few-bits hashing cannot be guaranteed due to severe information loss. |
| Approach: | They propose a simple unsupervised neural generative semantic hashing method with a focus on few-bits hash. |
| Outcome: | The proposed method improves on the state-of-the-art methods in few-bits hashing. |
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation (2022.findings-acl)
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| Challenge: | Chinese Grammatical Error Detection is a non-automatic method to detect grammatical errors in texts. |
| Approach: | They propose a Conditional Non-Autoregressive Error Generation model for Chinese grammatical errors that uses a masking and prediction method to generate a context-dependent error. |
| Outcome: | The proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGAD-2020 benchmarks. |
Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning (2025.findings-emnlp)
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| Challenge: | Existing methods for in-context learning (ICL) performance rely on quality and ordering of demonstrations. |
| Approach: | They propose a method that models iterative demonstration selection as a Markov Decision Process and craft hybrid reward signals. |
| Outcome: | The proposed method combines outcome-based accuracy signals with process-oriented signals like stepwise influence and label entropy improvement. |
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP (2024.findings-naacl)
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| Challenge: | a low-resource dataset is limited in training data, so generating task-specific data is challenging. |
| Approach: | They propose a data augmentation technique that prompts off-the-shelf instruction-following Large Language Models to generate augmentations. |
| Outcome: | The proposed technique outperforms baselines on 11 datasets spanning 3 tasks and 3 low-resource settings. |
Rectifying Demonstration Shortcut in In-Context Learning (2024.naacl-long)
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| Challenge: | Large language models (LLMs) can solve tasks with a few demonstrations, but often rely on their pre-trained semantic priors rather than the input-label relationships to proceed with ICL prediction. |
| Approach: | They propose a demonstration-aware calibration method to improve LLMs' ability to learn new input-label relationships from demonstrations. |
| Outcome: | The proposed method improves the original ICL task and the task learning setting, and the results are generalized across three LLM families. |
Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)
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| Challenge: | Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded. |
| Approach: | They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models . |
| Outcome: | The proposed model is the first few-shot NER dataset and the largest human-crafted NER data set. |
Improving Passage Retrieval with Zero-Shot Question Generation (2022.emnlp-main)
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Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer
| Challenge: | Existing re-ranking methods for open-domain question answering are not domain- or task-specific. |
| Approach: | They propose a simple and effective re-ranking method for improving passage retrieval in open-domain question answering. |
| Outcome: | The proposed method outperforms strong supervised models on open-domain questions and triviaQA datasets on top-1000 passages. |
Text Augmentation in a Multi-Task View (2021.eacl-main)
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| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. |
| Approach: | They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach . |
| Outcome: | The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results. |
Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
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| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
| Approach: | They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. |
| Outcome: | The proposed method has errors that are 5 to 13% lower than state-of-the-art models and is even more pronounced in scarce label setting. |
2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP). |
| Approach: | They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence. |
| Outcome: | The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms. |
Improving In-Context Few-Shot Learning via Self-Supervised Training (2022.naacl-main)
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Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, Zornitsa Kozareva
| Challenge: | Existing approaches to improve in-context few-shot learning are pretraining and downstream fewshot evaluation. |
| Approach: | They propose to use self-supervision as an intermediate training stage between pretraining and downstream fewshot usage to train models to perform in-context few shot learning. |
| Outcome: | The proposed model outperforms baseline models on two benchmarks. |
Latent Reasoning for Low-Resource Question Generation (2021.findings-acl)
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| Challenge: | Existing approaches to multihop question generation require extensive data collection and decomposition. |
| Approach: | They propose a generative approach that optimizes the two-phase model without question decomposition data. |
| Outcome: | The proposed approach outperforms baselines on HOTPOTQA, a benchmark multi-hop question answering dataset. |
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022.coling-1)
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| Challenge: | Existing methods for dialog routing are mostly heuristic and cannot achieve high-quality performance. |
| Approach: | They propose a multi-task learning framework with a dialog encoder and two tailored gated mechanism modules to solve this problem. |
| Outcome: | The proposed model can play the role of hierarchical information filtering and is non-invasive to existing dialog systems. |
Zero-shot Text Classification via Reinforced Self-training (2020.acl-main)
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Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
| Challenge: | Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks. |
| Approach: | They propose a self-training based method to efficiently leverage unlabeled data. |
| Outcome: | The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset. |
Query-focused Referentiability Learning for Zero-shot Retrieval (2025.naacl-long)
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| Challenge: | Existing dense representations have shown limitations in zero-shot scenarios . however, passage representations fail to align with their gold queries . |
| Approach: | They propose a query-focused concept of 'referentiable' which ensures passage representations are referenced by their gold queries. |
| Outcome: | The proposed model outperforms existing models on the BEIR benchmark. |
BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting (2025.findings-naacl)
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Mohammad Jahid Ibna Basher, Md Kowsher, Md Saiful Islam, Rabindra Nath Nandi, Nusrat Jahan Prottasha, Mehadi Hasan Menon, Tareq Al Muntasir, Shammur Absar Chowdhury, Firoj Alam, Niloofar Yousefi, Ozlem Garibay
| Challenge: | Empirical evaluations in few-shot settings show that BnTTS significantly improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech. |
| Approach: | They propose to integrate Bangla into a multilingual TTS pipeline with modifications to account for the phonetic and linguistic characteristics of the language. |
| Outcome: | The proposed framework improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech compared to state-of-the-art systems. |
Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (2023.acl-long)
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| Challenge: | Existing unsupervised approaches for learning knowledge graphs require multiple modules and require entity information or relation type for training. |
| Approach: | They propose a method that uses a unified pretrained language model to achieve fully unsupervised graph-text mutual conversion for the first time. |
| Outcome: | The proposed method outperforms state-of-the-art methods for G2T and T2G tasks by fine-tuning only one pretrained model. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
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| Challenge: | GUI automation is a key challenge in dynamic environments. |
| Approach: | They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs. |
| Outcome: | The proposed GUI-explorer shows significant improvements over existing agents. |
Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)
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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang
| Challenge: | Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored. |
| Approach: | They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data. |
| Outcome: | The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models. |
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain (2025.coling-main)
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| Challenge: | Existing research focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. |
| Approach: | They evaluate the efficacy of hybrid search across a variety of retrieval models in the french language . they find that fusion of different domain-general models consistently enhances performance . |
| Outcome: | The proposed model improves in-domain performance compared to a single model in a zero-shot context . the proposed model also improves when the models are trained in- domain . |
Regularization of Distinct Strategies for Unsupervised Question Generation (2020.findings-emnlp)
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| Challenge: | Unsupervised question answering (UQA) is a task of answering questions from a context that contains the answer. |
| Approach: | They propose a method to generate higher-quality questions with a teacher-student architecture and a regularization module to avoid bias toward a particular question generation strategy. |
| Outcome: | The proposed method generates higher-quality questions across diverse datasets and tasks and can be used to create a model with few-shot learning. |
Parallel Data Augmentation for Formality Style Transfer (2020.acl-main)
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| Challenge: | Formality style transfer is a task of automatically transforming text in one particular formality style into another. |
| Approach: | They propose to augment parallel data with three specific data augmentation methods to improve the model's generalization ability and reduce the overfitting risk. |
| Outcome: | The proposed methods significantly improve performance when used to pre-train the model and lead to the state-of-the-art results in the GYAFC benchmark dataset. |
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
| Approach: | They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets. |
Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers (2026.findings-acl)
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Van Yang, Shouren Wang, Debargha Ganguly, Xinpeng Li, Chaoda Song, Vikash Singh, Vipin Chaudhary, Xiaotian Han
| Challenge: | Explicit /think> tags are used to expose intermediate reasoning and enable hybrid thinking behaviors. |
| Approach: | They propose a training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, outperforming fixed-token and prompt-based baselines in terms of the accuracy–length trade-off. |
| Outcome: | The proposed method outperforms fixed-token and prompt-based prompts in accuracy–length trade-offs while improving Qwen3-8B on AIME from 69.8% to 72.4% and on GPQA from 58.5% to 61.1%. |
Disfluency Correction using Unsupervised and Semi-supervised Learning (2021.eacl-main)
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Nikhil Saini, Drumil Trivedi, Shreya Khare, Tejas Dhamecha, Preethi Jyothi, Samarth Bharadwaj, Pushpak Bhattacharyya
| Challenge: | Disfluencies in conversational speech can affect performance of downstream NLP tasks. |
| Approach: | They propose a disfluency correction model that converts disfluent to fluent text . they use unsupervised encoder-decoder models to generate semi-supervised models . |
| Outcome: | The proposed model achieves a BLEU score of 79.39 on the Switchboard corpus test set and 85.28 with semi-supervision. |
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks (2020.findings-emnlp)
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| Challenge: | Recent datasets heuristically choose examples to ensure label balance . state-of-the-art models trained on QQP and WikiQA have only 2.4% average precision . |
| Approach: | They show that recent datasets heuristically choose examples to ensure label balance . they instead use active learning to retrieve uncertain points from a large pool of unlabeled utterance pairs . |
| Outcome: | The proposed model improves on QQP and WikiQA by using more informative negative examples. |
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (2020.coling-main)
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| Challenge: | Existing approaches to improve unsupervised Question Answering (UQA) are expensive and require additional datasets. |
| Approach: | They propose an unsupervised QA approach that generates QA training data automatically. |
| Outcome: | The proposed method improves unsupervised QA significantly across a number of QA tasks. |
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language (2020.lrec-1)
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| Challenge: | Inuktitut is one of the 60 Indigenous languages currently spoken in Canada . polysynthetic languages are often termed agglutinative when their morphemes have clear boundaries and thus are easily segmentable. |
| Approach: | They propose to use a corpus of 23 hours of transcribed oral stories to train automatic speech recognition in Inuktitut. |
| Outcome: | The proposed model shows that Inuktitut displays a much higher degree of polysynthesis than other agglutinative languages like Finnish or Turkish. |
Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised Chinese word segmentation exploit shallow semantic information, which can miss important context. |
| Approach: | They propose to take advantage of deep contextual semantic information with a self-training manner to transform it into explicit word segmentation ability. |
| Outcome: | The proposed approach achieves state-of-the-art F1 score on two CWS benchmark datasets. |
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)
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| Challenge: | Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations. |
| Approach: | They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain. |
| Outcome: | The proposed framework is more effective than previous domain adaptation methods on three datasets. |
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)
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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang
| Challenge: | Existing approaches to learn a model from labeled data are expensive or prohibitive. |
| Approach: | They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain. |
| Outcome: | The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin. |
Semi-Supervised Formality Style Transfer with Consistency Training (2022.acl-long)
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| Challenge: | Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. |
| Approach: | They propose a semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. |
| Outcome: | The proposed framework can achieve state-of-the-art results even with less than 40% of the parallel data. |
Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German (2020.lrec-1)
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| Challenge: | Despite the small pool of speakers, there are still few natural language processing corpora, studies or tools for Swiss German. |
| Approach: | They propose to use a web scraper to generate the largest Swiss German text corpus . they show that the tool can be applied to other low-resource languages as well . |
| Outcome: | The proposed tool significantly improves language modeling in Swiss German, the authors show . |
Semi-Supervised Domain Adaptation for Emotion-Related Tasks (2023.findings-acl)
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| Challenge: | Semi-supervised domain adaptation (SSDA) is a model trained from a label-rich source domain to a new but related domain with a few labels of target data. |
| Approach: | They propose to decompose the semi-supervised domain adaptation framework into two subcomponents of unsupervised domain adaption (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target. |
| Outcome: | The proposed method is based on the co-learning of multiple classifiers for computer vision tasks and is published in the journal Nature. |
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
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Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
Active Learning Principles for In-Context Learning with Large Language Models (2023.findings-emnlp)
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| Challenge: | In-context learning has significantly enhanced predictive performance in few-shot learning settings. |
| Approach: | They propose to use pool-based Active Learning to identify the most informative demonstrations for few-shot learning over a single iteration to identify best demonstrations. |
| Outcome: | The proposed model outperforms all other methods, including random sampling, in the analysis of 24 classification and multi-choice tasks. |
Word Reordering for Zero-shot Cross-lingual Structured Prediction (2021.emnlp-main)
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| Challenge: | Current sentence encoders are word order sensitive, resulting in poor performance . Adapting word order from one language to another is key in cross-lingual structured prediction. |
| Approach: | They propose a new module to organize words following the source language order . they build structured prediction models with bag-of-words inputs and introduce a module to do this . |
| Outcome: | The proposed model significantly improves target language performance for languages that are distant from the source language. |
DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (2023.findings-acl)
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| Challenge: | Prompt-based methods have shown their efficacy in transferring general knowledge within pre-trained language models (PLMs) however, when applied to zero-shot entity and relation extraction, they struggle with the limited coverage of verbalizers to labels and the slow inference speed. |
| Approach: | They propose a method which reformulates zero-shot tasks into token discrimination tasks without having to construct verbalizers. |
| Outcome: | The proposed method outperforms baselines on two zero-shot entity recognition datasets with higher inference speed and achieves 7.5% improvement over previous state-of-the-art models on Wiki-ZSL and FewRel. |
Continual Few-shot Event Detection via Hierarchical Augmentation Networks (2024.lrec-main)
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| Challenge: | Existing methods for continual few-shot event detection use labeled data, but in real-world applications, new event types emerge continually. |
| Approach: | They propose a memory-based framework for continual few-shot event detection . they incorporate prototypical augmentation into the memory set to memorize previous event types . |
| Outcome: | The proposed method outperforms existing methods in multiple continual few-shot event detection tasks. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Recent work has demonstrated that in-context learning for dialogue state tracking outperforms training methods in the few-shot setting. |
| Approach: | They propose a method for in-context learning for dialogue state tracking that takes into account probabilities of competing surface forms and produces a more accurate dialogue state prediction. |
| Outcome: | The proposed method outperforms trained methods in the few-shot setting and requires little data and zero parameter updates. |
Logical DA: Enhancing Data Augmentation for Logical Reasoning via a Multi-Agent System (2025.findings-acl)
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| Challenge: | Existing data augmentation paradigms isolate data synthesis from label validation, thereby reducing their utility for complex reasoning tasks. |
| Approach: | They propose a framework for enhancing reasoning-focused data augmentation in few-shot learning scenarios that integrates four agents through two synergistic phases: diverse data generation and label verification. |
| Outcome: | The proposed framework achieves the highest average improvement in task accuracy in both fine-tuning and in-context learning paradigms. |
Zero-shot Script Parsing (2022.coling-1)
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| Challenge: | Existing resources cover only a small number of tasks, limiting its practical usefulness. |
| Approach: | They propose a zero-shot learning approach to script parsing which enables us to acquire script knowledge without domain-specific annotations. |
| Outcome: | The proposed model outperforms a previous model with scenario-specific supervision and achieves 68.1/74.4 average F1 for event / participant parsing. |
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru (2020.lrec-1)
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| Challenge: | Existing methods for extracting text from PDF files are expensive and limited by the absence of web content of endangered languages. |
| Approach: | They propose a method for creating monolingual corpora for four endangered languages . they use a PDF file format with multilingual sentences and noisy pages . |
| Outcome: | The proposed method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays. |
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering (2021.naacl-main)
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| Challenge: | et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge. |
| Approach: | They propose a method that retrieves and reranks evidence facts jointly . they propose to account for links between sentences and coverage with the given query . |
| Outcome: | The proposed approach achieves state-of-the-art evidence retrieval performance on two multi-hop question answering datasets. |
Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
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| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents (2025.coling-main)
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| Challenge: | Inspirational quotes from famous individuals are powerful tools that convey wisdom and insight in a concise and often figurative manner. |
| Approach: | They propose a context-based quote extraction system that aims to predict the most relevant quote from a long text. |
| Outcome: | The proposed system improves on a dataset with 5.08% BoW F1-score. |
SkillSpan: Hard and Soft Skill Extraction from English Job Postings (2022.naacl-main)
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| Challenge: | Existing studies on Skill Extraction (SE) use crowd-sourced labels or annotations from a predefined skill inventory. |
| Approach: | They propose a dataset that contains 14.5K sentences and over 12.5K annotated spans. |
| Outcome: | The proposed model outperforms non-adapted models and single-task outperformed multi-task learning. |
Zero-Shot Cross-Lingual Transfer with Meta Learning (2020.emnlp-main)
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| Challenge: | There are more than 7,000 languages spoken in the world, over 90 of which have more than 10 million native speakers each. |
| Approach: | They propose to use meta-learning to train a model on multiple languages at the same time . they use standard supervised, zero-shot cross-lingual, and few-shot crosses-lingual settings for different natural language understanding tasks. |
| Outcome: | The proposed setup improves on the state-of-the-art for a total of 15 languages. |
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)
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| Challenge: | Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability but their large size results in high inference latency. |
| Approach: | They propose an unsupervised domain adaptation framework that employs knowledge distillation to achieve domain-invariant representations at each layer. |
| Outcome: | The proposed framework outperforms early exit methods and domain adaptation methods under domain shift scenarios. |
Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements (2024.findings-acl)
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| Challenge: | Large language models demonstrate remarkable ability for learning to solve new tasks from a few examples. |
| Approach: | They propose to use templates to aggregate model predictions across multiple templates to improve model performance. |
| Outcome: | The proposed model ensembles boost model predictions while being robust to the choice of random set of templates. |
Data Augmentation with Atomic Templates for Spoken Language Understanding (D19-1)
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| Challenge: | Existing methods to enlarge SLU data require large amounts of labelled data. |
| Approach: | They propose a data augmentation method with atomic templates for Spoken Language Understanding which generates atomic exemplars from atomic template. |
| Outcome: | The proposed method improves on a DSTC 2&3 dataset which is a domain adaptation setting of SLU. |
Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)
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| Challenge: | Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains. |
| Approach: | They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on 21 datasets. |
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in information extraction. |
| Approach: | They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. |
| Outcome: | The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country . |
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
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| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
Extremely Weakly-supervised Text Classification with Wordsets Mining and Sync-Denoising (2024.naacl-long)
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| Challenge: | Existing methods for weakly-supervised text classification use only class names as supervision . Existing approaches to classify texts without labeled data have significant flaws, including zero-shot instability and context-dependent ambiguities. |
| Approach: | They propose to use wordsets to generate pseudo-labels for unlabeled texts . they propose to train the classifier using a hybrid learning strategy called sync-denoising . |
| Outcome: | The proposed method outperforms all existing prompt and seed methods on 11 datasets by an impressive average of 8 points. |
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging (2020.emnlp-main)
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| Challenge: | Existing studies on DA tagging focus on human-human social conversations, which is less applicable for task-oriented setting. |
| Approach: | They propose a controllable mechanism that augments text input by leveraging the pre-trained Mask token from BERT model. |
| Outcome: | The proposed mechanism augments text input by leveraging the pre-trained Mask token from BERT model. |
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation (2024.naacl-long)
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| Challenge: | State-of-the-art rankers pre-trained on large task-specific training data such as MS-MARCO exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. |
| Approach: | They propose a method to generate unsupervised domain adaptation for ranking using large-scale task-specific training data such as MS-MARCO and Wikipedia retrieval. |
| Outcome: | The proposed method outperforms all zero-shot baselines and significantly outperfies the SOTA baselines on 16 out of 18 datasets, for an average of 4% relative improvement across all datasets. |
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)
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| Challenge: | Question Answering (QA) is a field of increasing demand due to the availability of information online. |
| Approach: | They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence. |
| Outcome: | The proposed approach improves the performance of a QA model on generated pseudo-training data. |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models for extractive reading comprehension are not good at deciding whether no answer is presented in the context. |
| Approach: | They propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. |
| Outcome: | The proposed model performs better on the SQuAD 2.0 dataset than the baseline model and the BERT-large model. |
A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
| Approach: | They evaluate the performance of large language models and their generation strategies in 11 different languages using 3 NLP tasks and 4 open-source LLMs. |
| Outcome: | The proposed generation strategies and their combinations yield strong results across 11 languages, including several extremely low-resource ones. |
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
| Approach: | They propose a weakly supervised model that requires only the final answer as supervision to solve math word problems. |
| Outcome: | The proposed model achieves accuracy gains of 4.5% and 32% over current weakly-supervised methods on standard Math23K and AllArith datasets. |
Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)
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| Challenge: | Existing DA methods for named entity recognition (NER) are costly and labor-intensive to acquire, necessitating innovative approaches to data scarcity. |
| Approach: | They propose an order-agnostic data augmentation solution that exploits the order-based property in the training phase of sequence-to-sequence NER methods for data augmented. |
| Outcome: | The proposed method significantly enhances the few-shot capabilities of pre-trained language models in low-resource settings. |
Self-training Improves Pre-training for Natural Language Understanding (2021.naacl-main)
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Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Veselin Stoyanov, Alexis Conneau
| Challenge: | Unsupervised pretraining has led to improvements in natural language understanding . a data augmentation method can be used to generate labels for unlabeled examples . |
| Approach: | They propose a semi-supervised method which uses unlabeled data to retrieve sentences from a database of billions of unlabed sentences crawled from the web. |
| Outcome: | The proposed method improves on standard text classification benchmarks by 2.6% and knowledge distillation by few shots. |
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)
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| Challenge: | Experimental results show that crowdsourced annotations are highly effective under supervised conditions. |
| Approach: | They propose an annotator-aware representation learning model that is inspired by domain adaptation methods which attempt to capture effective domain-alike features. |
| Outcome: | The proposed model is highly effective on a benchmark dataset and achieves state-of-the-art performance with only a very small scale of expert annotations. |
Unsupervised Domain Adaptation for Joint Information Extraction (2022.findings-emnlp)
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| Challenge: | Current JIE methods focus on standard supervised learning setting where training and test data come from the same domain. |
| Approach: | They propose a method to induce domain-invariant representations for the tasks in JIE by a generalized version of domain-adversarial learning. |
| Outcome: | The proposed method improves out-of-domain performance for current pipeline approaches for all IE tasks. |
Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation (2025.acl-long)
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| Challenge: | Existing supervised fine-tuning (SFT) methods focus on directly generating the target output without leveraging the benefits of intermediate steps or initial guidance. |
| Approach: | They propose a task-agnostic framework that enables models to generate intermediate "warmup" sequences that are iteratively refined to maximize their contribution to the final output. |
| Outcome: | The proposed framework outperforms traditional supervised fine-tuning methods on translation, summarization, and multi-choice question answering tasks. |
Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations (2024.lrec-main)
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| Challenge: | Existing models do not provide an efficient way to locate information that enters the common ground. |
| Approach: | They propose a method based on segmentation of a conversation into themes followed by their summarization and obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. |
| Outcome: | The proposed method is based on the segmentation of a conversation into themes followed by their summarization and obtains the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. |
In-Context Learning with Iterative Demonstration Selection (2024.findings-emnlp)
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| Challenge: | Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample . Existing studies have shown that the optimal selection dimension, i.e., diversity or similarity, is task-specific. |
| Approach: | They propose to use zero-shot chain-of-thought reasoning to iteratively select examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. |
| Outcome: | The proposed method outperforms existing demonstration selection methods on reasoning, question answering, and topic classification tasks. |
A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)
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| Challenge: | Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability. |
| Approach: | They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor. |
| Outcome: | The proposed method outperforms baseline methods by 6%-16% in F1 scores. |
Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
| Outcome: | The proposed methods outperform state-of-the-art models trained with labeled data and large-scale unlabeled samples for learning. |
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization (2024.findings-acl)
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| Challenge: | Large language models (LLMs) can improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. |
| Approach: | They propose to use Prompt Chaining and Stepwise Prompting to perform iterative refinement . they propose to combine the two methods to produce a more favorable outcome . |
| Outcome: | The proposed methods can improve summary quality by mirroring a human-like iterative process . the results show that the prompt chaining method produces a more favorable outcome . |
Mitigating Demonstration Bias through Global Coevolutionary Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for chain-of-thought prompting rely on manual demonstrations . experimental results show that GCR outperforms baseline methods without performance degradation . |
| Approach: | They propose a method that uses random samples to generate demonstrations in zero-shot settings. |
| Outcome: | The proposed method outperforms baseline methods on ten datasets without demonstration bias. |
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% . |
Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
Continual Few-Shot Learning for Text Classification (2021.emnlp-main)
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| Challenge: | a large number of end-to-end systems are needed for many tasks in natural language processing. |
| Approach: | They propose a continual few-shot learning task where a system is asked to correct mistakes with a few training examples. |
| Outcome: | The proposed task compares two NLI and one sentiment analysis datasets with baselines from diverse paradigms. |
Unsupervised Multi-hop Question Answering by Question Generation (2021.naacl-main)
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| Challenge: | Existing training data for multi-hop question answering (QA) is time-consuming and resource-intensive. |
| Approach: | They propose an unsupervised framework that generates human-like multi-hop training data from homogeneous and heterogeneously data sources. |
| Outcome: | The proposed framework achieves 61% and 83% of the supervised learning performance for the HybridQA and HotpotQA datasets. |
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021.naacl-main)
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| Challenge: | State-of-the-art abstractive summarization models rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. |
| Approach: | They propose to use domain adaptation methods to simulate the low-resource domain adaptation setting for abstractive summarization systems with existing datasets across six diverse target domains. |
| Outcome: | The proposed model can be used to adapt to a low-resource domain adaptation setting. |
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)
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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai
| Challenge: | Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training. |
| Approach: | They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks. |
| Outcome: | The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning. |
Self-Supervised Prompt Optimization (2025.findings-emnlp)
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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo
| Challenge: | Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. |
| Approach: | They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples. |
Dynamic Knowledge Prompt for Chest X-ray Report Generation (2024.lrec-main)
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| Challenge: | Existing methods for radiology report generation fail to incorporate prior knowledge . data bias, sparse features of chest X-ray image make it difficult to generate reports . |
| Approach: | They propose a dynamically integrated framework for chest X-ray report generation that incorporates pulmonary lesion knowledge at the instance-level. |
| Outcome: | The proposed framework can dynamically incorporate pulmonary lesion knowledge at instance-level to facilitate report generation. |
Culture-TRIP: Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement (2025.naacl-long)
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| Challenge: | Existing text-to-image models fail to produce appropriate images for cultural concepts or objects not well known or underrepresented in western cultures, such as 'hangari' (a Korean utensil). |
| Approach: | They propose a method which iteratively refines the prompt to improve the alignment between the generated images and underrepresented cultural nouns in text-to-image models. |
| Outcome: | The proposed approach improves the alignment between the generated images and cultural nouns in text-to-image models. |
MCapsNet: Capsule Network for Text with Multi-Task Learning (D18-1)
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| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Multi-Source Domain Adaptation with Mixture of Experts (D18-1)
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| Challenge: | Existing methods for domain adaptation from multiple sources are designed to transfer supervision from a single source domain. |
| Approach: | They propose to capture the relationship between a target example and different source domains by a point-to-set metric. |
| Outcome: | The proposed method outperforms baselines and can handle negative transfer. |
MILU: A Multi-task Indic Language Understanding Benchmark (2025.naacl-long)
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| Challenge: | Existing benchmarks focus on English, leaving substantial gaps in assessing LLM capabilities in low-resource and linguistically diverse languages. |
| Approach: | They propose a multi-task indic language understanding benchmark to assess LLMs in low-resource languages. |
| Outcome: | The new benchmark spans 8 domains and 41 subjects across 11 Indic languages, reflecting general and culturally specific knowledge. |
Weakly Supervised Word Segmentation for Computational Language Documentation (2022.acl-long)
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| Challenge: | a recent paper aims to improve the effectiveness of unsupervised language analysis techniques in low resource settings. |
| Approach: | They propose to use a weak supervision to improve linguistic segmentation in low resource languages . they propose to provide linguists with LTs that can be used to create interactive annotation tools . |
| Outcome: | The proposed models can be used to improve the quality of language segmentation in low resource languages. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
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| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
KHANQ: A Dataset for Generating Deep Questions in Education (2022.coling-1)
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| Challenge: | Existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning. |
| Approach: | They propose a dataset for question generation that contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
| Outcome: | The proposed dataset contains 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. |
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection (2025.naacl-long)
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| Challenge: | In-context learning is sensitive to the choice of demonstrations and can be used for tasks with few examples. |
| Approach: | They propose a framework for in-context learning with noisy, pseudo-annotated demonstrations . they annotate large quantities of demonstrations in a zero-shot first pass . |
| Outcome: | The proposed framework outperforms ICL on biomedical NED datasets with zero human-annotation. |
“Well, Keep Thinking”: Enhancing LLM Reasoning with Adaptive Injection Decoding (2025.findings-acl)
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| Challenge: | Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a decoding strategy that nudges LLMs to continue reasoning, thereby preventing immature reasoning processes. |
| Outcome: | The proposed method significantly improves LLM reasoning capabilities on diverse reasoning benchmarks. |
Design Challenges in Low-resource Cross-lingual Entity Linking (2020.emnlp-main)
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| Challenge: | Existing techniques for grounding mentions of entities in a foreign language do not rise to the challenges introduced by text in low-resource languages (LRL) and fail to generalize to text not taken from Wikipedia, on which they are usually trained. |
| Approach: | They propose a cross-lingual XEL technique that uses search engines to locate and search for foreign language entries in Wikipedia. |
| Outcome: | The proposed system shows an increase of 25% in gold candidate recall and 13% in end-to-end linking accuracy over state-of-the-art baselines. |
Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages (2023.findings-acl)
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| Challenge: | Multilingual pretrained language models (MPLMs) perform strongly in cross-lingual transfer. |
| Approach: | They propose to augment context with similar sentences retrieved from a high-resource language (HRL) they find a significant correlation between cross-lingual transfer performance and similarity between high- and low-resourced languages . |
| Outcome: | The proposed model outperforms finetuning by 3.7% on three downstream tasks with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled and labeled settings. |
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation (2024.acl-long)
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| Challenge: | Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data. |
| Approach: | They propose a method that uses a dynamic graph to organize auxiliary languages in prompts to improve LRL translations. |
| Outcome: | The proposed method improves translation accuracy in low-resource languages (LRLs) using auxiliary language pairs and synthetic pseudo-parallel data. |
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning (2023.findings-acl)
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Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig, Chunting Zhou
| Challenge: | In-context learning-based evaluators are competitive with learned evaluation frameworks for text summarization tasks. |
| Approach: | They propose to use large language models as multi-dimensional evaluators using in-context learning to evaluate text summarization tasks. |
| Outcome: | The proposed frameworks are competitive with existing frameworks on relevance and factual consistency, the authors show . |
A Sentiment Consolidation Framework for Meta-Review Generation (2024.acl-long)
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| Challenge: | Recent advances in abstractive text summarization have created plausible summaries, but it is unclear if they truly possess the capability of information consolidation to generate summary. |
| Approach: | They propose to prompt large language models to generate meta-reviews and use evaluation metrics to assess the quality of generated meta- reviews. |
| Outcome: | The proposed framework proves that human meta-reviewers follow a framework of sentiment consolidation to write meta- reviews compared with prompting them with simple instructions. |
Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation (2022.coling-1)
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| Challenge: | Existing research on multi-hop question generation (QG) has not been done due to its complexity. |
| Approach: | They propose a type-dependent prompt cycleQAG with a cycle consistency loss . they propose to use the question type and words related to the correct answer as prompts . |
| Outcome: | The proposed model outperforms the baseline model by 10.38% based on ROUGE score. |
MVP: Multi-task Supervised Pre-training for Natural Language Generation (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have achieved remarkable success in natural language generation tasks. |
| Approach: | They propose to use a large-scale natural language generation corpus to pre-train a text generation model MVP in a supervised manner. |
| Outcome: | The proposed model outperforms BART and Flan-T5 on 13 out of 17 datasets and outperformed BART by 9.3% and FlaN-T5. |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods to improve Question Answering performance on non-English data are expensive and limited to evaluation set. |
| Approach: | They propose a method to improve Question Answering performance without additional annotations by leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. |
| Outcome: | The proposed method outperforms baselines on four datasets in English significantly . the proposed model outperformed baselines in english and is comparable to the validation set of the original SQuAD. |
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval (2021.emnlp-main)
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| Challenge: | Using self-training to train unsupervised domains can be expensive, resulting in poor generalization due to distributional shift. |
| Approach: | They propose to use unaligned data to train unsupervised domain adaptation models using cheap synthetically generated labeled data. |
| Outcome: | The proposed method significantly outperforms self-training on question generation and passage retrieval domains and on MLQuestions and PubMedQA. |
Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction (2024.findings-acl)
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| Challenge: | Document collections of various domains share some underlying collection-wide structure . structure can be useful in various use cases across different domains, such as legal, medical, or financial . |
| Approach: | They propose to identify the typical structure of document within a collection by using header paraphrases to ground topics to respective document locations. |
| Outcome: | The proposed method extracts meaningful collection-wide structure from documents in three domains in English and Hebrew. |
A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)
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| Challenge: | Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs. |
| Approach: | They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation . |
| Outcome: | The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets. |
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning (2024.findings-acl)
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| Challenge: | Existing methods to select unlabeled examples for annotation require a long time due to their complexity, hindering their practical viability. |
| Approach: | They propose a graph-based selection method to efficiently identify high-quality instances while minimizing computational overhead. |
| Outcome: | The proposed method significantly reduces selection time and improves performance on different tasks. |
Few-Shot Learning with Siamese Networks and Label Tuning (2022.acl-long)
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| Challenge: | Recent studies have shown that few-shot text classification is a poor solution for training data-intensive tasks. |
| Approach: | They propose a method that embeds texts and labels into classifiers with proper pre-training. |
| Outcome: | The proposed approach reduces inference cost by increasing the number of labels and embeddings. |
C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)
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| Challenge: | Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples. |
| Approach: | They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. |
| Outcome: | The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks. |
Harvesting and Refining Question-Answer Pairs for Unsupervised QA (2020.acl-main)
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| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
| Approach: | They propose two approaches to improve unsupervised question answering . first, they harvest lexically and syntactically divergent Wikipedia questions to automatically construct a corpus of question-answer pairs . second, they take advantage of the QA model to extract more appropriate answers . |
| Outcome: | The proposed approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. |
Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)
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| Challenge: | Existing methods to generate valid and fluent questions from text are limited and insufficient for training. |
| Approach: | They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text. |
| Outcome: | The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements. |
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages (2024.findings-emnlp)
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| Challenge: | Existing studies on multilingual automatic post-editing systems for low-resource Indo-Aryan languages have focused on different models for different language pairs. |
| Approach: | They propose to use a multilingual automatic post-editing system to improve machine translations for low-resource Indo-Aryan languages. |
| Outcome: | The proposed model outperforms English-Hindi and English-Marathi models by 2.5 and 2.39 TER points. |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |
Semi-supervised Contextual Historical Text Normalization (2020.acl-main)
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| Challenge: | Historical text normalization is the task of mapping historical word forms to their modern counterparts. |
| Approach: | They propose to use a generative normalization model to obtain contextualization from the target-side language model. |
| Outcome: | et al., 2018) show that the most effective approach reduces manual normalization time and manual training costs. |
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation (2024.acl-long)
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Tejpalsingh Siledar, Swaroop Nath, Sankara Muddu, Rupasai Rangaraju, Swaprava Nath, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera
| Challenge: | Existing evaluation methods for opinion summarizations lack adequate opinion summary evaluation datasets. |
| Approach: | They propose a dataset that combines 7 dimensions crucial to opinion summaries . they propose OP-I-PROMPT, a dimension-independent prompt, and OP PROMPTS, . |
| Outcome: | The proposed model achieves a Spearman correlation of 0.70 with human judgments, surpassing prior methods. |
Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025.coling-main)
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| Challenge: | Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions. |
| Approach: | They propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. |
| Outcome: | The proposed approach achieves state-of-the-art results in few-shot NER . it combines label supervision, cluster size constraints, and domain-specific discriminative subspace selection. |
Digitizing Nepal’s Written Heritage: A Comprehensive HTR Pipeline for Old Nepali Manuscripts (2026.acl-long)
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| Challenge: | Using a line-level transcription approach, we explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy for Old Nepali manuscripts. |
| Approach: | They propose a line-level transcription approach and explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy. |
| Outcome: | The proposed model achieves a 4.9% error rate and is highly reliable. |
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)
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| Challenge: | Existing methods for detecting LLM-generated text require no training data. |
| Approach: | They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts. |
| Outcome: | The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets. |
Unsupervised Domain Clusters in Pretrained Language Models (2020.acl-main)
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| Challenge: | Existing methods to select domains from large corpus of data are often over-simplistic and vague. |
| Approach: | They propose to use pre-trained language models to learn sentence representations that cluster by domains without supervision. |
| Outcome: | The proposed methods outperform established methods on domain selection and precision and recall with respect to an oracle selection. |
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks (2023.findings-acl)
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| Challenge: | Recent studies focus on retrieval to solve knowledge-intensive tasks, but the potential of retrieval for non-knowledge-intensive (NKI) tasks remains under-explored. |
| Approach: | They propose a task-agnostic retrieval framework for NKI tasks that uses a static index and a prompt-guided reranker to re-rank the nearest evidence according to task-specific relevance. |
| Outcome: | The proposed framework outperforms state-of-the-art retrieval-augmented methods on NKI tasks and will be released for further research. |
Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)
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Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang
| Challenge: | Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks. |
| Approach: | They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter. |
| Outcome: | The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA. |
Enhancing Cross-lingual Prompting with Dual Prompt Augmentation (2023.findings-acl)
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| Challenge: | a recent study shows that prompting is superior for multilingual/cross-lingual problems . despite its effectiveness on English tasks, its potential for cross-lingual problem is under-explored . |
| Approach: | They propose a framework for prompting that can be used to augment cross-lingual prompts. |
| Outcome: | The proposed framework achieves 46.54% with only 16 English training examples per class, significantly better than fine-tuning. |
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation (2022.emnlp-main)
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| Challenge: | Existing models for task-specific natural language generation do not contain any labeled examples. |
| Approach: | They propose a variational autoencoder with disentanglement priors for task-specific natural language generation with none or a handful of task-related labeled examples. |
| Outcome: | The proposed model outperforms baseline models in terms of data augmentation and text style transfer in the few-shot setting. |
Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions? (2025.findings-acl)
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| Challenge: | Existing research shows LLMs struggle with complex instructions involving multiple constraints. |
| Approach: | They propose a framework to divide complex instructions into single constraints and prepare appropriate tools to verify responses. |
| Outcome: | The proposed framework doubles Llama3.1-8B’s constraint adherence and triples Mistral-7B’ s performance. |
Quality Control for Crowdsourced Bilingual Dictionary in Low-Resource Languages (2022.lrec-1)
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| Challenge: | a recent study shows that crowdsourcing is becoming mainstream to create bilingual dictionaries . the number of people who can speak multiple low-resource languages is limited and the average ability of workers is low. |
| Approach: | They propose a method to aggregate the answers of evaluation tasks by majority voting . they use hyper questions to evaluate the reliability of workers and task-allocation method to select high-quality workers . |
| Outcome: | The proposed method improves quality of bilingual dictionaries by integrating answers by majority voting. |
Prompting open-source and commercial language models for grammatical error correction of English learner text (2024.findings-acl)
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Christopher Davis, Andrew Caines, O Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
| Challenge: | Recent advances in generative AI have enabled us to prompt large language models (LLMs) to produce texts which are fluent and grammatical. |
| Approach: | They evaluate model performance by measuring their performance on established benchmarks. |
| Outcome: | The proposed models outperform supervised English GEC models on fluency correction benchmarks and commercial LLMs on edit benchmarks. |
TheRuSLan: Database of Russian Sign Language (2020.lrec-1)
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| Challenge: | The database is the first of a kind for Russian sign language and is intended for use in machine learning, gesture recognition and sign language linguistics. |
| Approach: | They present a Russian sign language multimedia database called TheRuSLan . the database includes lexical units from Russian sign languages within one subject area . |
| Outcome: | The proposed database includes lexical units from Russian sign language within one subject area. |
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
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| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
| Approach: | They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances. |
| Outcome: | The proposed task significantly improves cost-effective zero-shot performance by large margins. |
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora (2021.emnlp-main)
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| Challenge: | Existing question generation methods rely on large amounts of synthetically generated datasets and costly computational resources. |
| Approach: | They propose a framework for domain adaptation that combines question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora. |
| Outcome: | The proposed framework improves on state-of-the-art questions in a domain with limited text corpora. |
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems (2025.emnlp-main)
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| Challenge: | Conventional LLM-based MAS rely on explicit coordination, e.g., prompts or voting, risking premature homogenization. |
| Approach: | They propose to preserve partial diversity by combining in-context learning with explicit coordination to form consensus in dynamic environments. |
| Outcome: | The proposed model outperforms explicit consensus models on three scenarios showing that partial deviation from group norms boosts exploration, robustness, and performance. |
Journalism-Guided Agentic In-context Learning for News Stance Detection (2025.emnlp-main)
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| Challenge: | Existing stance detection research on news content is limited to short texts and high-resource languages. |
| Approach: | They propose a dataset for article-level stance detection that integrates viewpoints into recommendation algorithms and a framework that employs a language model agent to predict the stances of key structural segments. |
| Outcome: | The proposed framework outperforms existing methods in identifying article stances and uncovering patterns of media bias. |
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection (2024.findings-acl)
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| Challenge: | Large language models generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. |
| Approach: | They propose to use a teacher model to label LLM generations and use their label probabilities to identify a representative subset of diverse generations that boost zero-shot accuracies while being efficient. |
| Outcome: | The proposed models generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. |
Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026.findings-acl)
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Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, Erik Cambria
| Challenge: | Existing large language models are limited in understanding, reasoning, calculation, and generation, limiting their performance in complex reasoning and dynamic tasks. |
| Approach: | They propose a plug-and-play framework that integrates a small-scale LLM (as agent) with large-scale large-level LLMs (a as environment) they propose generating prompts that are used to interact with LLM, and a double constraint reward that optimizes correctness and quality of generation. |
| Outcome: | The proposed framework significantly outperforms baseline large-scale large-language models across various tasks. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data (2023.findings-emnlp)
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| Challenge: | Chain-of-thought (CoT) prompting is a new approach to prompt large language models (LLMs) but most studies rely on human-annotated rational chains to prompt LLMs . |
| Approach: | They propose a method that augments rational chains from a small labeled dataset and pruning low-quality chains to construct a pool of machine generated rationale chains based on the labels. |
| Outcome: | The proposed method can bypass human engineering of CoT by automatically augmenting rational chains from a small labeled dataset, and pruning low-quality chains to construct a candidate pool of machine generated rationale chains based on the labels. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. |
| Approach: | They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. |
| Outcome: | The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages. |
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA (2023.emnlp-main)
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| Challenge: | In-Context Learning with Large Language Models (LLMs) has shown great performance on reasoning tasks. |
| Approach: | They propose a method for selecting a set of exemplars that is representative and diverse. |
| Outcome: | The proposed method outperforms existing methods on FinQA and TAT-QA on hybrid questions. |
Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation (2023.findings-acl)
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| Challenge: | Existing methods for supervised domain adaptation of machine translation focus on fine-tuning, which is non-extensible. |
| Approach: | They propose to perform unsupervised domain adaptation in a non-parametric manner by using in-domain monolingual data and performing nearest neighbour inference on both forward and backward directions. |
| Outcome: | The proposed method significantly improves the in-domain translation performance and achieves state-of-the-art results among non-parametric methods. |
Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates. |
| Approach: | They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance. |
| Outcome: | The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time. |
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking (2023.findings-acl)
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| Challenge: | Existing methods for annotating long-document question answering are based on short documents and can hardly incorporate long-range information. |
| Approach: | They propose an unsupervised method to generate long-document question answering pairs . they propose a method to aggregate and generate answers with long-range dependency . |
| Outcome: | The proposed method outperforms existing methods on NarrativeQA and Qasper. |
Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)
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| Challenge: | Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs. |
| Approach: | They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text. |
| Outcome: | The proposed pipeline outperforms existing parsers and baselines in syntactic analysis. |
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting (2023.emnlp-main)
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| Challenge: | Existing approaches to open-world relation extraction assume that all instances of unlabeled data belong to novel classes. |
| Approach: | They propose a method that classifies relations from known and novel classes within unlabeled data. |
| Outcome: | The proposed method outperforms existing methods on Open-world RE benchmarks. |
Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning (2024.emnlp-main)
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| Challenge: | In-Context-learning and few-shot prompting are viable methods for compositional output generation but they are sensitive to the choice of support examples. |
| Approach: | They propose a method which generates supports and targets current state of the world and then uses them in-context-learning to solve a query. |
| Outcome: | The proposed agent improves performance on a previously unsolved compositional generalization test without loss of performance in other areas. |
Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning (2024.findings-acl)
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| Challenge: | Recent studies have focused on instruction tuning to show cross-lingual generalization . a novel non-English meta-dataset is used to study instruction tuning . |
| Approach: | They perform instruction tuning individually for two distinct language meta-datasets and assess the performance on unseen tasks in a non-English language. |
| Outcome: | The proposed model outperforms baseline training in English and Korean by 20.7% and 13.6%. |
BenNumEval: A Benchmark to Assess LLMs’ Numerical Reasoning Capabilities in Bengali (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in general-purpose tasks but struggle with numerical reasoning, especially in low-resource languages like Bengali. |
| Approach: | They propose a benchmark to assess LLMs on numerical reasoning tasks in Bengali. |
| Outcome: | The proposed benchmark assesses LLMs on numerical reasoning tasks in Bengali. |
Token Alignment via Character Matching for Subword Completion (2024.findings-acl)
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Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Robert Kwiatkowski, Ramesh Nallapati, Parminder Bhatia, Bing Xiang
| Challenge: | Generative models struggle with prompts corresponding to partial tokens due to tokenization, where partial token is out-of-distribution during inference. |
| Approach: | They propose a method to alleviate tokenization artifact on text completion by backtracking to the last complete tokens and aligning subsequent generations to match with the prompt. |
| Outcome: | The proposed method shows that it improves on partial token scenarios with only a minor time increase. |
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors (2024.lrec-main)
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| Challenge: | Existing methods to learn new relations with limited labeled data are prone to catastrophic forgetting and overfitting. |
| Approach: | They propose a framework that uses prompts to acquire more generalized knowledge . they propose CFRE to continuously learn new relations while retaining knowledge of old ones . |
| Outcome: | The proposed method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios. |
Selective Prompting Tuning for Personalized Conversations with LLMs (2024.findings-acl)
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| Challenge: | Personalization in conversational AI requires persona profiles and contextual understanding to create meaningful conversations. |
| Approach: | They propose a method that softly prompts LLMs for personalized conversations in a selective way. |
| Outcome: | The proposed approach improves response diversity by up to 90% on the CONVAI2 dataset. |
From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)
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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu
| Challenge: | Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning. |
| Approach: | They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model. |
| Outcome: | The proposed method improves egocentric reasoning abilities on six tasks. |
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition (2024.emnlp-main)
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| Challenge: | Prior research addresses generating attributions alongside responses in open domains, either per sentence or per paragraph. |
| Approach: | They propose a method to decompose generated answers for attribution using template-based in-context learning. |
| Outcome: | The proposed approach enhances the semantic understanding of abstractive and extractive answers. |
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning (2025.emnlp-main)
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| Challenge: | Understanding the complex event ontology, extracting domain-specific triggers from the passage, and structuring them appropriately overloads and limits the utility of Large Language Models (LLMs). |
| Approach: | They propose a divergent-convergent reasoning framework that decouples the task of ED using Dreamer and Grounder. |
| Outcome: | The proposed framework outperforms baselines on six datasets across five domains and nine LLMs, achieving 4–7% average gains over the best baseline. |
Causal Intervention-based Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
| Approach: | They propose a causal intervention-based few-shot named entity recognition method that blocks the backdoor path between context and label. |
| Outcome: | The proposed method achieves state-of-the-art in a few-shot named entity recognition (NER) task. |
LLM Agents at the Roundtable: A Multi-Perspective and Dialectical Reasoning Framework for Essay Scoring (2025.findings-emnlp)
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| Challenge: | a new framework for automated essay scoring is needed to achieve multi-perspective understanding and judgment. |
| Approach: | They propose a roundtable essay scoring framework that performs precise and human-aligned scoring under a zero-shot setting. |
| Outcome: | The proposed framework outperforms previous zero-shot AES approaches by enabling collaboration among agents with diverse evaluation perspectives. |
Video-Text Prompting for Weakly Supervised Spatio-Temporal Video Grounding (2024.emnlp-main)
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| Challenge: | Existing methods extract each candidate tube feature independently by cropping objects from video frame feature, discarding all contextual information such as position change and inter-entity relationship. |
| Approach: | They propose to use video-text prompts to construct candidate feature instead of cropping tube region from feature map . they also propose negative contrastive samples whose candidate object is erased instead of being highlighted . |
| Outcome: | The proposed methods surpass existing weakly-supervised methods by a great margin . they draw visual markers over objects tubes as video prompts . |
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting. |
| Approach: | They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting. |
| Outcome: | The proposed model outperforms existing methods under zero-shot setting on three datasets. |
Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents (2026.acl-long)
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| Challenge: | Existing red-team methods rely on modifying user prompts, which lack adaptability to new data and may impact the agent’s performance. |
| Approach: | They propose a framework that implicitly manipulates the agent’s reasoning trajectory and memory retrieval with three key stages: Trigger Extraction, Reasoning Hijacking, and Constraint Tightening. |
| Outcome: | The proposed framework shows outstanding performance in cross-model and cross-scenario environments. |
Task Facet Learning: A Structured Approach To Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing approaches to prompt optimization are limited to learning multiple facets of a task from training examples. |
| Approach: | They propose to optimize a text prompt by considering different facets of a task and including them in the prompt. |
| Outcome: | The proposed algorithm can generate long, complex prompts that existing methods are unable to generate. |
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer’s Disease Detection (2025.acl-long)
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| Challenge: | Existing methods for in-context learning (ICL) perform poorly for AD diagnosis due to inherent complexity of task. |
| Approach: | They propose a demonstration selection strategy that leverages a delta score to assess the relative gains of each training example and a KNN-based retriever that dynamically selects optimal “representatives” for a given input. |
| Outcome: | The proposed model outperforms existing methods on two AD detection datasets and surpasses even supervised classifiers. |
RAG-Zeval: Enhancing RAG Responses Evaluator through End-to-End Reasoning and Ranking-Based Reinforcement Learning (2025.emnlp-main)
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| Challenge: | Existing evaluation frameworks rely on direct prompting of resource-intensive models with complex multi-stage prompts, introducing significant computational cost and underutilizing models’ reasoning capabilities. |
| Approach: | They propose a framework that trains evaluators with reinforcement learning to generate comprehensive and sound assessments with detailed explanation in one-pass. |
| Outcome: | The proposed framework outperforms baseline evaluation frameworks that rely on LLMs with 10-100 more parameters and achieves the strongest correlation with human judgments. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |
Strengthening the WiC: New Polysemy Dataset in Hindi and Lack of Cross Lingual Transfer (2024.lrec-main)
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| Challenge: | a new study addresses the problem of natural language processing in low-resource languages such as Hindi . the paper focuses on Word Sense Disambiguation, a fundamental NLP task that deals with polysemous words. |
| Approach: | They propose a Hindi WSD dataset that allows training and testing of contextualized models. |
| Outcome: | The proposed dataset enables training and testing of contextualized models in Hindi . the results show that the proposed dataset can handle polysemy tasks in low-resource languages . |
PUER: Boosting Few-shot Positive-Unlabeled Entity Resolution with Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Existing approaches to entity resolution focus on supervised learning, but manual annotation is labor-intensive. |
| Approach: | They propose an end-to-end ER solution that leverages Large Language Models in PU learning setting to address low-resource entity resolution. |
| Outcome: | The proposed solution improves the performance of PUER on a positive-unlabeled learning environment. |
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment (2026.acl-long)
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| Challenge: | Existing methods for training reasoning-oriented large language models assume high-resource settings with abundant data. |
| Approach: | They propose a framework that integrates high-value general-domain data to promote more diverse exploration. |
| Outcome: | The proposed framework matches or surpasses RLVR trained with 32 target-domain samples using 32 target domain samples. |
Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing studies on ICL for Named Entity Recognition (NER) have mainly explored few-shot settings, but the potential of scaling to hundreds of demonstrations has not been thoroughly investigated. |
| Approach: | They evaluate various LLMs across multiple domains using hundreds of ICL examples and then assess the feasibility of using many-shot ICL as a data annotation framework. |
| Outcome: | The proposed framework can be scaled to hundreds of examples and annotate and refining data for low-resource NER tasks. |
Unsupervised Grouping of Public Procurement Similar Items: Which Text Representation Should I Use? (2024.lrec-main)
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Pedro P. V. Brum, Mariana O. Silva, Gabriel P. Oliveira, Lucas G. L. Costa, Anisio Lacerda, Gisele Pappa
| Challenge: | establishing reference prices is essential to guide competitors in setting product prices . however, selecting an appropriate representation for text is challenging . |
| Approach: | They propose a framework for text cleaning, extraction, and representation based on sentence representations for public procurement item descriptions. |
| Outcome: | The proposed approach captures the most important components of item descriptions. |
Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar constructions remains unclear. |
| Approach: | They propose a benchmark for deterministic finite automata (DFA) construction from regular languages, comprising factual knowledge questions, seen construction problems from public sources, and unseen problems. |
| Outcome: | The proposed model achieves perfect accuracy on factual questions and 84-90% on seen tasks, but falls sharply on unseen problems (by 30-64%), with failures stemming from systematic misinterpretation of language constraints, incorrect handling of Kleene-star semantics, and a failure to preserve global consistency. |
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis (2024.lrec-main)
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Md. Arid Hasan, Shudipta Das, Afiyat Anjum, Firoj Alam, Anika Anjum, Avijit Sarker, Sheak Rashed Haider Noori
| Challenge: | Recent performance of Large Language Models (LLMs) in low-resource languages is under-researched due to resource constraints. |
| Approach: | They present a manually annotated dataset encompassing 33,606 Bangla tweets and Facebook comments. |
| Outcome: | The proposed model outperforms other models even in zero and few-shot scenarios. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |
TTVS: Boosting Self-Exploring Reinforcement Learning via Test-time Variational Synthesis (2026.findings-acl)
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| Challenge: | Existing test-time methods are limited in specialized or novel domains where supervision is prohibitively expensive or unavailable. |
| Approach: | They propose a framework that augments training stream from unlabeled test queries. |
| Outcome: | Extensive experiments show TTVS outperforms state-of-the-art RL-based techniques on unlabeled test-time data. |
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)
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| Challenge: | Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training. |
| Approach: | They propose a framework that enhances zero-shot slot inference through robust prompt alignment. |
| Outcome: | Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. |
The Pitfalls of KV Cache Compression (2026.acl-long)
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| Challenge: | Recent literature has shown minimal degradation of KV cache in multi-instruction prompts . authors show that certain instructions degrade much more rapidly with compression . |
| Approach: | They propose to change KV cache eviction policies to reduce the impact of KV evict bias . they propose to use a 'simple' evviction policy to reduce ejection bias if the LLM is a multi-instruction model . |
| Outcome: | The proposed methods show that certain instructions degrade much faster with compression, causing them to be ignored by the LLM. |
Incomplete In-context Learning (2026.acl-long)
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Wenqiang Wang, Wen Yujia, Yan Xiao, Zhifeng Chen, Yangshijie Zhang, Peng Chen, Mingbo Yang, Xiaochun Cao
| Challenge: | Existing in-context learning assumes the retrieval dataset contains demonstrations for all output label spaces. |
| Approach: | They propose a framework with train-free and train-based variants to address IICL . they propose to integrate a dataset with labeled demonstrations for each output space . |
| Outcome: | The proposed framework outperforms existing methods under incomplete retrieval datasets and even outperformed ICL with complete labels. |
Many-Shot Scaling of In-Context Learning with Self-Generated Demonstrations (2026.findings-acl)
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| Challenge: | In-context learning methods that use self-generated annotations do not scale to many-shot scenarios. |
| Approach: | They propose a framework analogous to semi-supervised learning that uses self-generated annotations instead of ground truth labels. |
| Outcome: | The proposed framework outperforms ground truth ICL under zero-shot, few-shot and many-shot settings. |
Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (2026.acl-long)
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| Challenge: | Existing Distantly Supervised Relation Extraction models rely on task-specific training, but their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. |
| Approach: | They propose a framework for distantly supervised relation extraction that uses a trained DSRE model to identify the top-k candidate relations for a given test sentence and a dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data. |
| Outcome: | The proposed framework achieves 20 F1 points gains in English and 17 F1 point gains on Indic languages over previous models and naive prompting baselines. |