FEW-SHOT

Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking (2023.findings-eacl)

Challenge:	Prompt tuning on a few data samples presents security issues, e.g., Trojan attacks.
Approach:	They propose a method to transfer established data poisoning attacks directly to few-shot prompt tuning, a technique to address the poisoned imbalance issue.
Outcome:	The proposed method achieves an ASR of over 99% while maintaining negligible decreases in CDA.

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Derek Chen, Kun Qian, Zhou Yu

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (2020.emnlp-main)

Challenge:	Prompt-based methods with large pre-trained language models have shown impressive unaided performance across many NLP tasks.
Approach:	They propose a meta-learning scheme to stabilize the ability of the model to perform well under various prompts and introduce a saliency model to limit dialogue text length.
Outcome:	The proposed model improves on large pre-trained language models with labeled in-context exemplars and can be used to generate more exemplar queries.

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Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)

Challenge:	Recent attempts to learn static representations of entities and references ignore their dynamic properties.
Approach:	They propose to learn static representations of entities and references ignoring their dynamic properties . a neighbor encoder learns entities' roles while a query-aware aggregator learns references' contributions .
Outcome:	The proposed approach achieves state-of-the-art results with different few-shot sizes.

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Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao

Are Prompt-based Models Clueless? (2022.acl-long)

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.

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Pride Kavumba, Ryo Takahashi, Yusuke Oda

A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)

Challenge:	Prompting has reduced the data requirement by reusing the language model head and formatting the task input to match the pre-training objective.
Approach:	They propose to examine whether few-shot prompt-based models exploit superficial cues by reusing the model head and formatting the input to match the pre-training objective.
Outcome:	The proposed models perform well on instances with superficial cues, but often outperform random accuracy on instances without superficial cuing.

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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan

Sentiment Analysis

Challenge:	Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment.
Approach:	They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks.
Outcome:	The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task.

Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats (2024.lrec-main)

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Philipp Heinrich, Andreas Blombach, Bao Minh Doan Dang, Leonardo Zilio, Linda Havenstein, Nathan Dykes, Stephanie Evert, Fabian Schäfer

Challenge:	Existing methods to identify and track conspiracy narratives are difficult to track and use because of their short-lived nature.
Approach:	They analysed 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators.
Outcome:	The proposed methods compare well with off-the-shelf methods and human performance.

Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)

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Jonas Golde, Felix Hamborg, Alan Akbik

Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)

Challenge:	Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text.
Approach:	They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition.
Outcome:	The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)

Challenge:	Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation.
Approach:	They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies.
Outcome:	The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task.

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Zeng Yang, Linhai Zhang, Deyu Zhou

Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition (2022.findings-emnlp)

Challenge:	Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting.
Approach:	They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data.
Outcome:	The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset.

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Zihan Wang, Kewen Zhao, Zilong Wang, Jingbo Shang

CAM 2.0: End-to-End Open Domain Comparative Question Answering System (2024.lrec-main)

Challenge:	Existing methods for fine-tuning pre-trained language models are limited . we propose a few-shot fine-uning framework for NER .
Approach:	They propose a few-shot fine-tuning framework for named entity recognition (NER) they propose three new types of tokens, "is-entity", "which-type" and "bracket"
Outcome:	The proposed framework improves on pre-trained language models on several benchmark datasets.

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Ahmad Shallouf, Hanna Herasimchyk, Mikhail Salnikov, Rudy Alexandro Garrido Veliz, Natia Mestvirishvili, Alexander Panchenko, Chris Biemann, Irina Nikishina

Natural Language Interfaces

Challenge:	Comparative Question Answering is a Natural Language Processing task that combines Question Answers and Argument Mining.
Approach:	They propose a system for answering comparative questions called CAM 2.0 and a public leaderboard called CompUGE that unifies existing datasets under a single easy-to-use evaluation suite.
Outcome:	The proposed system is compared with previous web-form-based systems . it features question identification, object and aspect labeling, stance classification, summarization . the proposed system has a user-friendly interface and is available for free on the web .

Coarse-to-fine Few-shot Learning for Named Entity Recognition (2023.findings-acl)

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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen

Information Extraction & Text Mining

Challenge:	Existing few-shot NER solutions do not consider sub-class discrimination and various granularity of new classes during coarse training.
Approach:	They propose a method that uses a cluster-based prototype loss to learn group-wise discriminative representations of coarse-grained classes and a mixture prototype loss for learning the representations.
Outcome:	The proposed method shows superior performance over baseline methods on in-domain and cross-domain settings with various target granularity.

Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models (2023.findings-acl)

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Fengzhu Zeng, Wei Gao

NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction (2022.coling-1)

Challenge:	Existing methods for few-shot and zero-shot fact verification require a large set of training data.
Approach:	They propose a method to prompt pre-trained language models to be consistent to improve the factuality assessment capability of PLMs.
Outcome:	The proposed method outperforms state-of-the-art few-shot fact verification models with a small number of unlabeled instances on zero-shot verification.

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Yi Sun, Yu Zheng, Chao Hao, Hangping Qiu

Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)

Challenge:	Recent studies have shown that using prompts to utilize language models to perform downstream tasks is more effective than using token-level methods such as PET.
Approach:	They propose to use a BERT original pre-training task abandoned by RoBERTa and other models to construct a sentence-level prompt-based method that does not need to fix the length of the prompt or the position to be predicted.
Outcome:	The proposed method performs better than PET and EFL on a BERT pre-training task and is comparable to other prompt-based methods.

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Tianyu Gao, Adam Fisch, Danqi Chen

FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation (2021.emnlp-main)

Challenge:	Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations.
Approach:	They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation .
Outcome:	The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks.

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Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal, Scott Yih, Yashar Mehdad, Srini Iyer

CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)

Challenge:	Pre-trained sequence to sequence models are effective in making and generating NL explanations, but they have many shortcomings.
Approach:	They propose a model that uses sentence markers to eliminate explanation fabrication . they use fusion-in-decoder architecture to handle long input contexts .
Outcome:	The proposed model significantly improves on the ERASER explainability benchmark.

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Yifei Yuan, Chen Shi, Wang Runze, Liyi Chen, Renjun Hu, Zengming Zhang, Feijun Jiang, Wai Lam

Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking (2022.findings-acl)

Challenge:	Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history .
Approach:	They propose to use unlabeled data to make further improvements using contrastive co-training paradigm.
Outcome:	The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios.

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Jamin Shin, Hangyeol Yu, Hyeongdon Moon, Andrea Madotto, Juneyoung Park

Dialogue Systems & Conversational Agents

Challenge:	Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process.
Approach:	They propose to reformulate dialogue state tracking as a dialogue summarization problem by using synthetic dialogue summaries generated by a set of rules.
Outcome:	The proposed method outperforms previous studies on few-shot dialogue state tracking in MultiWoZ 2.0 and 2.1 in cross-domain and multi-domain settings.

LegalViz: Legal Text Visualization by Text To Diagram Generation (2025.naacl-long)

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Eri Onami, Taiki Miyanishi, Koki Maeda, Shuhei Kurita

Multimodality

Challenge:	Graphviz provides diagrams for legal documents that are easy to understand and understand . a novel dataset of 23 languages and 7,010 cases of legal document and visualization pairs is proposed .
Approach:	They propose a dataset of legal diagrams using DOT graph description language of Graphviz.
Outcome:	The proposed dataset outperforms existing models including GPTs in 23 languages and 7,010 cases of legal document and visualization pairs.

Few-Shot (Dis)Agreement Identification in Online Discussions with Regularized and Augmented Meta-Learning (2022.findings-emnlp)

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Yuanyuan Lei, Ruihong Huang

Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning (2020.emnlp-main)

Challenge:	Existing annotated datasets do not cover all topics of interest.
Approach:	They propose a metric-based meta-learning approach that trains a meta-learner with two key abilities: decoding and generalizing domains.
Outcome:	The proposed approach can be quickly applied to analyze opinions for new topics with few labeled instances.

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Yi Yang, Arzoo Katiyar

Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text (2025.coling-main)

Challenge:	Named entity recognition (NER) is widely adopted in several domains, such as news, medical, and social media.
Approach:	They propose a few-shot named entity recognition system based on nearest neighbor learning and structured inference.
Outcome:	The proposed method improves F1 scores on standard few-shot NER evaluation tasks by 6% to 16% relative to previous methods.

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Ali Al Lawati, Jason Lucas, Prasenjit Mitra

Text Generation

Challenge:	Large Language Models (LLMs) have shown remarkable performance in various NLP tasks, including semantic parsing, which translates natural language into formal code representations.
Approach:	They propose a semantic captioning task to repurpose semantic parsing datasets for semantic captions.
Outcome:	The proposed model outperforms random selection and other methods by 39% on BLEU score.

Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)

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Zihao Li, Lecheng Zheng, Bowen Jin, Dongqi Fu, Baoyu Jing, Yikun Ban, Jingrui He, Jiawei Han

Multimodality

Challenge:	Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains.
Approach:	They propose a multi-modal prompt learning paradigm to adapt pre-trained GNNs to downstream tasks with weak text supervision.
Outcome:	The proposed model can generalize graphs to unseen classes with weak text supervision.

Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)

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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang

Information Extraction & Text Mining

Challenge:	Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility.
Approach:	They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step.
Outcome:	The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets.

Few-shot Unified Question Answering: Tuning Models or Prompts? (2023.findings-emnlp)

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Srijan Bansal, Semih Yavuz, Bo Pang, Meghana Bhat, Yingbo Zhou

WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction (2023.acl-long)

Challenge:	Question-answering (QA) tasks investigate specific question types, knowledge domains, or reasoning skills, leading to specialized models catering to specific categories of QA tasks.
Approach:	They propose to use model and prompt tuning for unified QA in a low-resource setting to overcome drawbacks of unified models.
Outcome:	The proposed model and prompt tuning paradigms outperform model tuning in a few-shot setting with a good initialization and achieve a significant performance boost from pre-training in 'low-resource' setting.

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Qiyu Wu, Masaaki Nagata, Yoshimasa Tsuruoka

Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques (2024.acl-long)

Challenge:	Existing word alignment methods rely on manual data and lack generalization ability.
Approach:	They propose to use a weakly-supervised large-scale weakly supervised dataset for word alignment pre-training via span prediction to reduce the need for manual data.
Outcome:	The proposed method improves upon the best supervised baseline by 3.3 6.1 points in F1 and 1.5 6.1 point in AER.

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Anar Yeginbergen, Maite Oronoz, Rodrigo Agerri

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

Challenge:	Recent work on sequence labelling has explored different strategies to mitigate the lack of manually annotated data for the large majority of the world languages.
Approach:	They propose to use the mask objective to exploit the few-shot capabilities of pre-trained language models to improve their performance.
Outcome:	The proposed model-transfer outperforms data-transference and fine-tuning outperformed few-shot methods for Argument Mining task.

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022.emnlp-main)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Zhe Hu, Hou Pong Chan, Lifu Huang

Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)

Challenge:	Recent pre-trained language models have produced impressive results, but there is still a gap between human written texts and machine-generated outputs.
Approach:	They propose a multi-task training strategy for long text generation grounded on the cognitive theory of writing.
Outcome:	The proposed model achieves better results on three open-ended generation tasks than baselines.

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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han

Adversarial Knowledge Stimulated Contrastive Prompting for Few-shot Language Learners (2023.findings-acl)

Challenge:	Existing methods to build named entity recognition systems with limited labeled data are lacking.
Approach:	They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited.
Outcome:	The proposed NER systems outperform existing methods on few-shot and training-free settings.

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Kai Zheng, Qingfeng Sun, Yaming Yang, Tengchao Lv, Yeyong Pi, Changlin Zhao, Fei Xu, Qi Zhang

Making Pre-trained Language Models Better Learn Few-Shot Spoken Language Understanding in More Practical Scenarios (2023.findings-acl)

Challenge:	Prompt-based fine-tuning has boosted performance of Pre-trained language models on few-shot Natural Language Understanding (NLU) tasks by employing task-specific prompts.
Approach:	They propose a Cloze-driven prompt framework for prompt tuning that implicitly stimulates knowledge from pre-trained language models.
Outcome:	The proposed framework outperforms state-of-the-art for prompt-based fine-tuning on few-shot NLU tasks.

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Yufan Wang, Jie Mei, Bowei Zou, Rui Fan, Tingting He, Ai Ti Aw

ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning (2024.findings-emnlp)

Challenge:	Existing few-shot Spoken Language Understanding models need to be trained on a set of data-rich source domains and adapt to the target domain with a few examples.
Approach:	They propose a scenario where only a pre-trained language model and a few labeled examples are used to train few-shot SLU models.
Outcome:	The proposed model outperforms existing models on few-shot settings by reducing the number of slot labels and reducing training complexity.

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Yu-Chen Lin, Wei-Hua Li, Jun-cheng Chen, Chu-Song Chen

Transfer-Aware Data Selection for Domain Adaptation in Text Retrieval (2025.findings-emnlp)

Challenge:	Prompt Tuning has been a popular fine-tuning method for large-scale pretrained language models.
Approach:	They propose a method that allows all soft prompts to share a set of learnable codebook vectors in each subspace, with each prompt differentiated by a number of adaptive weights.
Outcome:	The proposed method achieves superior performance on 17 diverse natural language tasks including natural language understanding (NLU) and question answering (QA) tasks by tuning only 0.3% of parameters of the PLMs.

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Linzhu Yu, Huan Li, Ke Chen, Lidan Shou

MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024.lrec-main)

Challenge:	Existing methods to improve domain adaptation do not guarantee improved adaptability, but may negatively impact model performance.
Approach:	They propose a framework that can effectively improve model adaptability by selecting beneficial data without evaluating all source data.
Outcome:	The proposed framework improves model adaptability by selecting beneficial data without evaluating all source data.

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Yutian Zhao, Huimin Wang, Xian Wu, Yefeng Zheng

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment (2026.acl-long)

Challenge:	Existing approaches to disease classification are limited in real-world clinics due to insufficient data and inflexibility.
Approach:	They propose a medical knowledge-Enhanced Contrastive Learning approach to disease diagnosis . they incorporate medical knowledge graphs and medical licensing exams in modeling .
Outcome:	The proposed model outperforms existing models on real clinical EMRs on a single patient.

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Zhanyu Liu, Qingguo Hu, Ante Wang, Chenqing Liu, Zhishang Xiang, Hui Li, Delai Qiu, Jinsong Su