-SHOT

SEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models (2022.findings-naacl)

Challenge:	Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting .
Approach:	They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models .
Outcome:	The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects.

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Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, Diyi Yang

Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)

Challenge:	Recent research shows promising results on combining pretrained language models with canonical utterance for few-shot semantic parsing.
Approach:	They propose a few-shot semantic parsing method that decomposes a problem into a sequence of sub-problems, which correspond to the sub-clauses of the formal language.
Outcome:	The proposed method achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split.

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Rui Zhang, Yangfeng Ji, Yue Zhang, Rebecca J. Passonneau

LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification (2022.naacl-main)

Challenge:	Current NLP models heavily rely on effective representation learning algorithms.
Approach:	This tutorial introduces contrastive learning and provides an introduction to the techniques.
Outcome:	This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them.

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S. K. Hong, Tae Young Jang

Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards (2022.findings-emnlp)

Challenge:	Existing few-shot text classification methods often lack labeled data in real-world tasks.
Approach:	They propose a meta-learning method that encodes how to attend for given tasks . they evaluate the method on five benchmark datasets and show it is competitive .
Outcome:	The proposed method performs better on five benchmark datasets than previous methods on labeled data.

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Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Natural Language to Code Generation in Interactive Data Science Notebooks (2023.acl-long)

Challenge:	Existing work does not take full advantage of over-parameterized characteristics of large pre-trained language models.
Approach:	They propose a method that uses frozen "thinned" networks to obtain a mixture of rewards and advance the derivative-free prompt learning.
Outcome:	The proposed method outperforms previous gradient-free prompt learning methods and achieves parity with gradient-based counterparts on seven language understanding benchmarks under few-shot settings.

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Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Oleksandr Polozov, Charles Sutton

Programming Languages in NLP

Challenge:	Data scientists use computational notebooks to perform data wrangling and analytic tasks.
Approach:	They build a benchmark program that synthesizes programs given NL intents from users by using a Python code language model.
Outcome:	The proposed model outperforms public code LMs in a dataset of 1078 code generation problems using the pandas data analysis framework in data science notebooks.

Literature Meets Data: A Synergistic Approach to Hypothesis Generation (2025.acl-long)

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Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, Chenhao Tan

Challenge:	Existing methods for hypothesis generation are theory-driven and data-driven, but they lack the computational power to complement each other.
Approach:	They develop a method that combines literature-based insights with data to perform LLM-powered hypothesis generation.
Outcome:	The proposed method outperforms baseline methods on five datasets and shows human accuracy improves on deception detection and AI generated content detection tasks.

Dataset Distillation with Attention Labels for Fine-tuning BERT (2023.acl-short)

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Aru Maekawa, Naoki Kobayashi, Kotaro Funakoshi, Manabu Okumura

Multi-Document Summarization with Centroid-Based Pretraining (2023.acl-short)

Challenge:	Specifically, we propose to introduce attention labels, which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities.
Approach:	They propose to introduce attention labels which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities.
Outcome:	The proposed methods perform impressively in four different NLP tasks and achieve 93.2% accuracy in AGNews, which is 98.5% of the original dataset even with only one sample per class and only one gradient step.

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Ratish Surendran Puduppully, Parag Jain, Nancy Chen, Mark Steedman

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)

Challenge:	In Multi-Document Summarization, the input is a set of documents, and the output is its summary.
Approach:	They propose a novel pretraining objective that uses the ROUGE-based centroid of each document cluster as a proxy for its summary.
Outcome:	The proposed model is better or comparable to state-of-the-art models.

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Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes (2024.naacl-short)

Challenge:	Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data.
Approach:	They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
Outcome:	The proposed language model generalizes well across knowledge-grounded dialogue tasks.

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Harmon Bhasin, Timothy Ossowski, Yiqiao Zhong, Junjie Hu

FPT: Feature Prompt Tuning for Few-shot Readability Assessment (2024.naacl-long)

Challenge:	Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential.
Approach:	They propose to combine multi-task learning (MTL) with in-context learning (ICL) to build models that can generalize to multiple tasks while being robust to out-of-distribution examples.
Outcome:	The proposed training strategies enable models to learn difficult tasks while mixing in prior tasks, denoted as mixed curriculum.

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Ziyang Wang, Sanwoo Lee, Hsiu-Yuan Huang, Yunfang Wu

FastFit: Fast and Effective Few-Shot Text Classification with a Multitude of Classes (2024.naacl-demo)

Challenge:	Prompt-based methods lack crucial linguistic knowledge for readability assessment tasks such as word length, sentence length, and usage of different difficulty-level words.
Approach:	They propose a new prompt-based tuning framework that incorporates linguistic knowledge and a loss function to calibrate the similarity ranking order between categories.
Outcome:	The proposed framework outperforms the large language model gpt-3.5-turbo-16k in most cases.

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Asaf Yehudai, Elron Bandel

LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging (2022.coling-1)

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.

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Andy Rosenbaum, Saleh Soltan, Wael Hamza, Yannick Versley, Markus Boese

LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking (2024.eacl-demo)

Challenge:	LINGUIST generates annotated data for Intent Classification and Slot Tagging (IC+ST) we demonstrate fine-tuning of a large-scale seq2seq model to control outputs of multilingual data generation.
Approach:	They propose a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST) they use a 5-billion-parameter multilingual sequence-to-sequence model to fine-tune it on a flexible instruction prompt.
Outcome:	The proposed method outperforms state-of-the-art approaches on a SNIPS intent setting and shows significant improvement on IC+ST in a cross-lingual setting.

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Fahim Dalvi, Maram Hasanain, Sabri Boughorbel, Basel Mousi, Samir Abdaljalil, Nizi Nazar, Ahmed Abdelali, Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Ali

Challenge:	Recent development and success of Large Language Models necessitate evaluation of their performance across diverse NLP tasks in different languages.
Approach:	They propose a framework that can be customized to evaluate LLMs for any NLP task, regardless of language.
Outcome:	The LLMeBench framework can be customized to evaluate LLMs for any NLP task, regardless of language.

BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra (2024.findings-acl)

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Parker Glenn, Parag Dakle, Liang Wang, Preethi Raghavan

OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

Challenge:	Existing hybrid question answering systems use a "prompt-and-pray" paradigm . context size limitations limit ability of many transformer-based LLMs to fit into a given prompt .
Approach:	They propose a superset of SQLite to act as a unified dialect for orchestrating reasoning across unstructured and structured data.
Outcome:	The proposed framework scales to massive datasets and improves performance while using 35% fewer tokens.

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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan

PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents (2024.eacl-long)

Challenge:	Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach:	They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome:	The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.

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Simeng Sun, Yang Liu, Shuohang Wang, Dan Iter, Chenguang Zhu, Mohit Iyyer

Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)

Challenge:	Using chain-of-thought prompting, large language models perform better on complex reasoning tasks.
Approach:	They propose a prompting framework that decomposes a question into a sequence of actions and executes them over the document to obtain the answer.
Outcome:	The proposed framework outperforms zero-shot and chain-of-thought prompting on a QuALITY dataset . it proposes a plan based on actions mined from a training set and executes it step by step .

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Xin Xu, Xiang Chen, Ningyu Zhang, Xin Xie, Xi Chen, Huajun Chen

Boosting Transformers and Language Models for Clinical Prediction in Immunotherapy (2023.acl-industry)

Challenge:	Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data.
Approach:	They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data.
Outcome:	The proposed methods perform well in low-resource settings with 8 relation extraction datasets.

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Zekai Chen, Mariann Micsinai Balan, Kevin Brown

Few-Shot Text Generation with Natural Language Instructions (2021.emnlp-main)

Challenge:	Current machine learning approaches to predict clinical outcomes are limited to tabular data and are not applicable to clinical prediction.
Approach:	They investigate the potential of transformers to improve clinical prediction compared to conventional machine learning approaches and address the challenge of few-shot learning in predicting rare disease areas.
Outcome:	The proposed model improves the accuracy of baseline models and language models under few-shot regimes and shows that it is more accurate than previous models.

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Timo Schick, Hinrich Schütze

LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory (2022.acl-short)

Challenge:	Existing approaches to text generation combine task descriptions and examples with supervised learning.
Approach:	They propose a method for text generation that is based on pattern-exploiting training.
Outcome:	The proposed approach improves on several summarization and headline generation datasets.

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Eunhwan Park, Donghyeon Jeon, Seonhoon Kim, Inho Kang, Seung-Hoon Na

AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments (2026.eacl-demo)

Challenge:	LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example.
Approach:	They propose to use auto-generated prompts and add demonstrations to LM-BFF to improve few-shot fine-tuning of language models with multiple soft demonstrations.
Outcome:	The proposed method improves few-shot fine-tuning on eight NLP tasks.

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Ario Saeid Vaghefi, Aymane Hachcham, Veronica Grasso, Nakiete Msemo, Chiara Colesanti Senni, Markus Leippold

EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning (2024.naacl-short)

Challenge:	EWS financial flows are opaque and lack standardized labels, structures, and terminology for EWS-related spending.
Approach:	They propose an agent-based Retrieval-Augmented Generation system that uses hybrid retrieval and internal chain-of-thought reasoning to extract relevant financial data and classify EWS investments.
Outcome:	The proposed system outperforms four alternatives on multi-label classification and budget allocation on an annotated CREWS Fund corpus.

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Raja Sekhar Reddy Mekala, Yasaman Razeghi, Sameer Singh

Intent Detection with WikiHow (2020.aacl-main)

Challenge:	Language models are adopting inference-time prompting techniques such as zero-shot and few-shot prompting.
Approach:	They propose a prompting technique that prompts the model to rephrase its queries before answering them.
Outcome:	The proposed prompt improves zero-shot-CoT performance of code-davinci-002 by 5% . the proposed prompt is tailored for four scenarios in both zero- and few-shot settings .

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Li Zhang, Qing Lyu, Chris Callison-Burch

A Closer Look at Few-Shot Out-of-Distribution Intent Detection (2022.coling-1)

Challenge:	Existing approaches to intent detection have limited data annotated for new domains or languages.
Approach:	They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions.
Outcome:	The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets.

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Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu, Albert Y.S. Lam

Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task (2022.acl-short)

Challenge:	Existing methods for few-shot out-of-distribution (OOD) intent detection are not adequate . despite its importance, few- shot OOD intent detection is a challenging problem .
Approach:	They propose a latent representation generation and self-supervision approach to solve few-shot OOD intent detection problem.
Outcome:	The proposed approach is highly effective and could improve state-of-the-art methods for few-shot OOD intent detection.

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Mohsen Tabasi, Kiamehr Rezaee, Mohammad Taher Pilehvar

Can language models learn from explanations in context? (2022.findings-emnlp)

Challenge:	Existing few-shot approaches fail on the semantic distinction task of the Word-in-Context dataset.
Approach:	They propose a prompt-based approach which boosts few-shot performance to the level of fully supervised methods by using similarity metrics.
Outcome:	The proposed technique boosts few-shot performance to the level of fully supervised methods.

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Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, Kory Mathewson, Mh Tessler, Antonia Creswell, James McClelland, Jane Wang, Felix Hill

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)

Challenge:	Language Models can adapt to a few in-context examples, but without training.
Approach:	They examine how explanations of few-shot examples can help Language Models (LMs) explanations can improve performance even without tuning, they find .
Outcome:	The proposed explanations outperform hand-tuned explanations on small validation sets.

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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang

Prompt2Model: Generating Deployable Models from Natural Language Instructions (2023.emnlp-demo)

Challenge:	Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress.
Approach:	They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability.
Outcome:	The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability.

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Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch, Tongshuang Wu, Graham Neubig

Semantic Span Annotation: An Exploratory Study of LLM Annotation (2026.acl-srw)

Challenge:	Large language models (LLMs) are a step backward from traditional special-purpose NLP models . they require extensive computational resources for deployment and can be gated behind APIs .
Approach:	They propose a general-purpose method that takes a natural language task description and uses it to train a special-purpose model.
Outcome:	The proposed method outperforms a strong LLM by 20% while being 700 times smaller.

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Tejas Goyal, Dhriti Krishnan, Anuj Gupta, Jaromir Savelka

Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest (2023.acl-long)

Challenge:	Structured span extraction research is siloed by context length, annotation task, and domain . Identifying a span within a natural language text and affixing it with a semantic label has been considered a core task in NLP .
Approach:	They propose a framework for structured span annotation that integrates five datasets under a common JSONL format with character-level offsets.
Outcome:	The proposed framework can generalize across four domains under three prompting configurations.

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Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi

Grounded Multimodal In-Context Learning for Product Weight Estimation at Scale in E-commerce (2026.acl-industry)

Challenge:	Large neural networks can generate jokes, but do they really “understand” humor? a new challenge challenges AI models to match a joke to a cartoon, identify a winning caption, and explain why a winner is funny.
Approach:	They propose three tasks based on the New Yorker Cartoon Caption Contest . they aim to match a joke to a cartoon, identify a winning caption and explain why it's funny .
Outcome:	The proposed tasks are based on the New Yorker Cartoon Caption Contest . they include matching a joke to a cartoon, identifying a winning caption, and explaining why a funny caption is funny.

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Bhavuk Singhal, Arsh Keshari, Ravindra Kumar Yadav

Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes (2021.tacl-1)

Challenge:	a large number of e-commerce platforms require manual verification and specialized hardware.
Approach:	They propose a multimodal weight estimation framework that uses category-specific exemplars to infer discretized weight buckets.
Outcome:	The proposed approach outperforms strong multimodal KNN baselines in accuracy and near-bucket reliability.

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Ofer Sabo, Yanai Elazar, Yoav Goldberg, Ido Dagan

Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples (2024.acl-srw)

Challenge:	a recent study has focused on few-shot learning (FSL) for relation classification, but it requires large amounts of training data.
Approach:	They propose a method for deriving more realistic few-shot test data from available datasets for supervised RC.
Outcome:	The proposed method yields a challenging benchmark for FSL-RC on which state of the art models show poor performance.

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Soma Sato, Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda

Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms (2025.acl-long)

Challenge:	Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing.
Approach:	They propose to automatically generate an NLI dataset with an LLM and use it for fine-tuning of PromptEOL.
Outcome:	The proposed model outperforms existing models on STS tasks without large manually annotated datasets.

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Rajvardhan Oak, Muhammad Haroon, Claire Wonjeong Jo, Magdalena Wojcieszak, Anshuman Chhabra

Hierarchical Attention Prototypical Networks for Few-Shot Text Classification (D19-1)

Challenge:	Social media platforms use machine learning and artificial intelligence to maximize user engagement, but can indirectly cause exposure to harmful content.
Approach:	They propose a re-ranking approach using Large Language Models to assess and rerank content sequences using large annotated data sets.
Outcome:	The proposed method significantly outperforms existing proprietary moderation methods on three datasets, three models and across three configurations.

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Shengli Sun, Qingfeng Sun, Kevin Zhou, Tengchao Lv

ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs (2026.findings-eacl)

Challenge:	Existing methods for text classification are based on large-scale labeled data, but few data are available.
Approach:	They propose a hierarchical attention prototypical networks for few-shot text classification . they use attention mechanism to highlight or weaken the importance of features, words, and instances .
Outcome:	The proposed model can capture more important features, words, and instances . it can also increase support set augmentability and accelerate convergence speed in training stage .

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Yassir Lairgi, Ludovic Moncla, Khalid Benabdeslem, Rémy Cazabet, Pierre Cléau

Reframing Instructional Prompts to GPTk’s Language (2022.findings-acl)

Challenge:	Unstructured data is expanding at an unprecedented rate, and static knowledge graphs are often overlooked due to their dynamic nature and lack of time-sensitive features.
Approach:	They propose a few-shot approach that builds and continuously updates Temporal Knowledge Graphs (TKGs) from unstructured texts.
Outcome:	Empirical results show that ATOM achieves 18% higher exhaustivity, 33% better stability, and over 90% latency reduction compared to baseline methods.

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Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi

Generating Complex Question Decompositions in the Face of Distribution Shifts (2025.naacl-long)

Challenge:	Using reframing techniques, we find that instructional prompts are easier to follow for Language Models (LMs)
Approach:	They propose reframing techniques for manual reformulation of prompts into more effective ones . they compare performance of LMs prompted with reframed instructions on 12 NLP tasks .
Outcome:	The reframing techniques used for prompt reformulation improve performance on 12 tasks . the techniques boost performance on LMs with different sizes compared with original prompts .

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Kelvin Han, Claire Gardent

Natural Language Interfaces

Challenge:	Question decomposition has been found to improve large language models’ (LLMs) performance on complex question answering (QA) however, performance on the task remains dominated by supervised approaches, suggesting room for making LLMs better decomposers.
Approach:	They propose to generate synthetic decomposition data with only five annotated examples by extending recent advances in using LLM-as-judge and for reranking in novel ways.
Outcome:	The proposed approach generates synthetic decomposition data with only five examples over two benchmark datasets.

Molecular String Representation Preferences in Pretrained LLMs: A Comparative Study in Zero- & Few-Shot Molecular Property Prediction (2025.emnlp-main)

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George Arthur Baker, Mario Sanz-Guerrero, Katharina von der Wense

Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)

Challenge:	Molecular property prediction plays a crucial role in medicinal chemistry . traditional machine learning approaches do not involve natural language .
Approach:	They compare performance of four state-of-the-art LLMs on molecular property prediction tasks . they find statistically significant zero- and few-shot preferences for InChI and IUPAC names .
Outcome:	The proposed model outperforms the current model on molecular property prediction tasks . the model's representation preferences are based on representation granularity, tokenization and prevalence in pretraining corpora .

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Young-Jun Lee, Chae-Gyun Lim, Ho-Jin Choi

Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation (2021.naacl-main)

Challenge:	Empathy is a multi-dimensional concept consisting of cognitive and affective aspects.
Approach:	They propose two new in-context example selection methods that utilize emotion and situational information.
Outcome:	The proposed method is effective in measuring the degree of human empathy.

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Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad

LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning (2026.acl-short)

Challenge:	Recent advances in summarization are driven by the availability of large datasets such as the CNN-DailyMail corpus and the New York Times corpus.
Approach:	They propose a method for fine-tuning pretrained models for summarization in unsupervised manner . they use Wikipedia data to produce pseudo-summaries which contain characteristics of target dataset .
Outcome:	The proposed method achieves state-of-the-art, zero-shot abstractive summarization performance on CNN-DailyMail dataset and compares with other methods on other datasets.

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Obed Junias, Maria Leonor Pacheco

Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness (2023.emnlp-industry)

Challenge:	LOGICAL-COMMONSENSEQA benchmarks evaluate commonsense reasoning as logical composition over pairs of atomic statements . commonsensible reasoning is central to human cognition and a long-standing challenge in artificial intelligence and natural language understanding.
Approach:	They propose a benchmark that reframes commonsense reasoning as logical composition over pairs of atomic statements using plausibility-level operators.
Outcome:	LOGICAL-COMMONSENSEQA exposes fundamental reasoning limitations and provides a framework for advancing compositional commonsense reasoning.

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Xiaoyu Tan, Shaojie Shi, Xihe Qiu, Chao Qu, Zhenting Qi, Yinghui Xu, Yuan Qi

Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models (2023.emnlp-main)

Challenge:	Recent studies have shown that large language models are useful, honest, harmless (HHH) however, RLHF requires high hardware resources and human efforts.
Approach:	They propose a framework that allows LLMs to align themselves with HHH . they use IF and reinforcement learning from human feedback to fine-tune their models .
Outcome:	The proposed framework achieves similar performance to RLHF and human-generated models with a minimal alignment tax.

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Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, Joonsuk Park, Jaewoo Kang

Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)

Challenge:	In open-domain question answering, users often ask ambiguous questions (AQs) . one approach is to identify all possible interpretations of the AQ and generate a long-form answer addressing them all.
Approach:	They propose a framework that generates a long-form answer addressing all possible interpretations of an ambiguous question.
Outcome:	The proposed framework outperforms baselines on ASQA in a few-shot setup across metrics while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambigo-ROUGE.

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Muhammad Khalifa, Muhammad Abdul-Mageed, Khaled Shaalan

TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue (2020.emnlp-main)

Challenge:	Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data.
Approach:	They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy.
Outcome:	The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging).

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Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, Caiming Xiong

Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification (2026.acl-srw)

Challenge:	Existing pre-trained language models with self-attention encoder architectures are less useful in practice.
Approach:	They propose to use user and system tokens to model dialogue behavior during pre-training . they propose a contrastive objective function to simulate the response selection task .
Outcome:	The proposed model outperforms baseline models on four downstream tasks . it also has a few-shot ability that can mitigate the data scarcity problem .

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Sercan Karakaş, Yusuf Şimşek

FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization (2022.naacl-main)

Challenge:	idiomatic light verb constructions (LVCs) are challenging for multiword expression processing . they share the same surface form as fully literal verb–object combinations .
Approach:	They frame Turkish LVC detection as a binary classification task . they compare a supervised Turkish encoder baseline to three instruction-tuned LLMs .
Outcome:	The proposed method improves Turkish LVC detection on a controlled set with matched negatives and positives.

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David Wan, Mohit Bansal

Summarization

Challenge:	Abstractive summarization models suffer from the problem of hallucinations, where a summary contains facts or entities not present in the original document.
Approach:	They propose an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning.
Outcome:	Experiments on three downstream tasks show that FactPEGASUS significantly improves factuality compared to the original pre-training objective in zero-shot and few-shot settings.

Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems (P19-1)

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Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, Caiming Xiong, Richard Socher, Pascale Fung

Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing (2023.tacl-1)

Challenge:	Existing approaches to dialogue state tracking are dependent on domain ontology and lack of sharing knowledge across domains.
Approach:	They propose a transferable dialogue state generator that generates dialogue states from utterances using copy mechanism.
Outcome:	Empirical results show that TRADE achieves state-of-the-art 48.62% joint goal accuracy for the five domains of MultiWOZ.

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Tom Sherborne, Tom Hosking, Mirella Lapata

Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction (2024.findings-eacl)

Challenge:	Existing work on cross-lingual semantic parsing has focused on English . a few-shot approach to parse from natural languages is comparatively unexplored .
Approach:	They propose a method that minimizes cross-lingual divergence between probabilistic latent variables by Optimal Transport.
Outcome:	The proposed method improves performance even without parallel input translations on two datasets.

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Prakamya Mishra, Lincy Pattanaik, Arunima Sundar, Nishant Yadav, Mayank Kulkarni

Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning (2024.findings-eacl)

Challenge:	Scientific domain-specific pre-training has led to state-of-the-art keyphrase extraction performance with a majority of benchmarks being within the domain.
Approach:	They propose to leverage topic information available in the data to build a clustering-based sampling approach that facilitates selecting a few samples to label from the target domain .
Outcome:	The proposed approach leads to 26.35 points in performance when compared to selecting few-shot samples uniformly at random.

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Lifu Tu, Jin Qu, Semih Yavuz, Shafiq Joty, Wenhao Liu, Caiming Xiong, Yingbo Zhou

El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)

Challenge:	Cross-lingual transfer of language models trained on high-resource languages such as English has been limited due to the high cost of obtaining non-English conversational data.
Approach:	They introduce a parallel and large-scale multilingual conversation dataset that is used for cross-lingual alignment pretraining by translating the English-only Schema-Guided Dialogue dataset into 105 other languages.
Outcome:	The proposed model performs well on slot-filling and intent classification tasks, and is able to perform well in other languages.

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Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, Sonal Gupta

Prompt-Based Meta-Learning For Few-shot Text Classification (2022.emnlp-main)

Challenge:	Code-switching (CS) is the alternation of languages within an utterance or conversation.
Approach:	They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available.
Outcome:	The proposed models improve when only English data is available alongside zero or a few CS training instances.

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Haoxing Zhang, Xiaofeng Zhang, Haibo Huang, Lei Yu

DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference (2021.naacl-main)

Challenge:	Existing methods to learn text labels require large amounts of data to build many few-shot tasks.
Approach:	They propose a Prompt-Based Meta-Learning model that adds the prompting mechanism to the meta-learning method.
Outcome:	The proposed method improves on four text classification datasets with high accuracy and robustness.

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Shikhar Murty, Tatsunori B. Hashimoto, Christopher Manning

Effective Few-Shot Classification with Transfer Learning (2020.coling-main)

Challenge:	Meta-learning has not yet succeeded in NLP due to the lack of a well-defined task distribution . meta-learners tend to overfit their adaptation mechanism and datasets are heterogeneous .
Approach:	They propose a method for decomposing datasets into Reasoning Categories to form additional high quality tasks.
Outcome:	The proposed method improves the accuracy of meta-learners by 1.5-4% across four few-shot NLI problems.

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Aakriti Gupta, Kapil Thadani, Neil O’Hare

AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation (2021.acl-long)

Challenge:	Recent work on few-shot learning addresses the problem of learning based on a small amount of training data.
Approach:	They adapt the Amazon Review Sentiment Classification (ARSC) text dataset for few-shot learning . they train a single binary classifier to learn all few- shot classes jointly .
Outcome:	The proposed approach outperforms most published results on the ARSC text dataset . the results suggest that the classes in the AR SC few-shot task are very similar to each other .

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Xinnuo Xu, Guoyin Wang, Young-Bum Kim, Sungjin Lee

Language Model Pre-Training with Sparse Latent Typing (2022.emnlp-main)

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.

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Liliang Ren, Zixuan Zhang, Han Wang, Clare Voss, ChengXiang Zhai, Heng Ji

Multi-Scale Prompt Memory-Augmented Model for Black-Box Scenarios (2024.naacl-long)

Challenge:	Modern large-scale Pre-trained Language Models focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences.
Approach:	They propose a new pre-training objective that enables the model to learn latent types . the objective allows the model a self-supervised way to extract sentence-level keywords .
Outcome:	The proposed model learns interpretable latent type categories without external knowledge and improves downstream tasks.

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Xiaojun Kuang, C. L. Philip Chen, Shuzhen Li, Tong Zhang

BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer (2024.naacl-long)

Challenge:	Existing methods for few-shot text classification require numerous LMs’ calls to search optimal prompts, thus resulting in overfitting performance and increasing computational cost.
Approach:	They propose a multi-scale knowledge prompt-based memory model that extracts instance-level and class-level knowledge and stores them in memory banks during training.
Outcome:	Experiments on different benchmarks and parameter analysis demonstrate the effectiveness and efficiency of MuSKPrompt in black-box few-shot text classification tasks.

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Akari Asai, Sneha Kudugunta, Xinyan Yu, Terra Blevins, Hila Gonen, Machel Reid, Yulia Tsvetkov, Sebastian Ruder, Hannaneh Hajishirzi

The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models (2025.findings-naacl)

Challenge:	Recent advances in few-shot generalization in natural language processing focus on English.
Approach:	They propose a benchmark that unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions.
Outcome:	The proposed framework unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions.

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Artem Kirsanov, Chi-Ning Chou, Kyunghyun Cho, SueYeon Chung

What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study (2023.findings-emnlp)

Challenge:	Recent studies have focused on the inputoutput behavior of LMs, leaving the internal mechanisms behind ICL largely unexplored.
Approach:	They investigate how different prompting methods modify internal representations in pre-trained language models.
Outcome:	The proposed model can be programmed with natural language to perform a wide array of tasks without expensive fine-tuning.

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Aman Madaan, Katherine Hermann, Amir Yazdanbakhsh

Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)

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.

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Zhenyu Zhang, Yuming Zhao, Meng Chen, Xiaodong He

Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)

Challenge:	a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning .
Approach:	They propose a label anchored contrastive learning approach for language understanding using a class label.
Outcome:	The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings .

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Subendhu Rongali, Mukund Sridhar, Haidar Khan, Konstantine Arkoudas, Wael Hamza, Andrew McCallum

ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages (2025.acl-industry)

Challenge:	Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations.
Approach:	They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data.
Outcome:	The proposed architecture outperforms existing models in low-resource settings.

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Mehant Kammakomati, Sameer Pimparkhede, Srikanth G. Tamilselvam, Prince Kumar, Pushpak Bhattacharyya

Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions (2022.findings-naacl)

Challenge:	Large Language Models (LLMs) have demonstrated potential in code generation and natural language understanding, but they struggle with code constraints.
Approach:	They propose to use Large Language Models to handle constraints represented in code . they use JSON, YAML, XML, Python, and natural language to test their effectiveness .
Outcome:	The proposed benchmark shows that LLMs can handle code constraints better than natural language . the results suggest that conscious choice of representations can lead to optimal use of LLM in enterprise use cases involving code constraints.

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Kosuke Nishida, Kyosuke Nishida, Shuichi Nishioka

The Art of Prompting: Event Detection based on Type Specific Prompts (2023.acl-short)

Challenge:	Humans can efficiently learn about new concepts from language descriptions, and we propose a new machine learning model, LIDE, which has a text decoder to generate the descriptions and a decoded text encoder to obtain the text representations of machine-generated descriptions.
Approach:	They propose a model with a text decoder to generate the descriptions and a corresponding text encoder to obtain the text representations of machine- or user-generated descriptions.
Outcome:	The proposed model outperforms baseline models with machine-generated descriptions and with high-quality user-generated models with high quality explanations.

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Sijia Wang, Mo Yu, Lifu Huang

GraphQL Query Generation: A Large Training and Benchmarking Dataset (2024.emnlp-industry)

Challenge:	Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited.
Approach:	They propose a unified framework to integrate event type specific prompts for supervised, few-shot and zero-shot event detection.
Outcome:	The proposed framework shows up to 22.2% gain over the prior state-of-the-art frameworks.

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Manish Kesarwani, Sambit Ghosh, Nitin Gupta, Shramona Chakraborty, Renuka Sindhgatta, Sameep Mehta, Carlos Eberhardt, Dan Debrunner

Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)

Challenge:	GraphQL is a powerful query language for APIs, but crafting complex GraphqL queries can be challenging.
Approach:	a team of researchers has created a large-scale, cross-domain text-to-GraphQL query operation dataset . the dataset includes 10,940 training triples spanning 185 cross-source data stores and 957 test triples over 14 data stores.
Outcome:	The proposed dataset includes 10,940 training triples and 957 test triples over 14 data stores.

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Guizhen Chen, Liying Cheng, Anh Tuan Luu, Lidong Bing

Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)

Challenge:	Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence.
Approach:	They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets .
Outcome:	The proposed model performs well on argument mining and argument generation tasks.

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Keqing He, Jinchao Zhang, Yuanmeng Yan, Weiran Xu, Cheng Niu, Jie Zhou

RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation (2023.acl-short)

Challenge:	Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness.
Approach:	They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations .
Outcome:	The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings.

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Gabriele Sarti, Phu Mon Htut, Xing Niu, Benjamin Hsu, Anna Currey, Georgiana Dinu, Maria Nadejde

Large language models are few-shot clinical information extractors (2022.emnlp-main)

Challenge:	Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of outputs.
Approach:	They propose a new approach to attribute-controlled translation that leverages multilingual language models to perform ACT in few-shot and zero-shot settings.
Outcome:	The proposed approach improves generation accuracy over the standard prompting approach in both zero-shot and few-shot settings.

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Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David Sontag

Event-Guided Denoising for Multilingual Relation Learning (2020.coling-main)

Challenge:	a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes.
Approach:	They propose to use large language models to tackle diverse clinical extraction tasks . they propose to reannote existing CASI datasets to compare their models with clinical text.
Outcome:	The proposed models outperform existing models on few-shot clinical information extraction tasks.

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Amith Ananthram, Emily Allaway, Kathleen McKeown

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

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 .

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

Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling (2022.naacl-main)

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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Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Daxin Jiang

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)

Challenge:	Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks.
Approach:	They propose a cross-lingual language informative span masking task to eliminate the objective gap between pre-training and fine-tuning stages.
Outcome:	The proposed method surpasses the state-of-the-art methods on multiple benchmarks even with limited pre-training data.

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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)

Challenge:	Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models.
Approach:	They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning.
Outcome:	The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.

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Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl

Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step (2023.acl-long)

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.

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Liunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang, Yejin Choi

A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification (2023.acl-short)

Challenge:	Symbolic Chain-of-thought Distillation (SCoTD) is a method to train a smaller student model on rationalizations sampled from a significantly larger teacher model.
Approach:	They propose a method to train a smaller student model on rationalizations from a larger teacher model.
Outcome:	The proposed method improves the performance of a student model in supervised and few-shot settings and especially for challenge sets.

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Rohan Bhambhoria, Lei Chen, Xiaodan Zhu

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)

Challenge:	Large language models have been able to perform well on benchmarks, but they are often not able address real-world challenges.
Approach:	They propose to refactor hierarchical tasks into a more indicative long-tail prediction task.
Outcome:	The proposed method does not require parameter updates and achieves strong performance across multiple datasets.

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Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, Maosong Sun

Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise (2024.findings-naacl)

Challenge:	Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training.
Approach:	They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning.
Outcome:	Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing.

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Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng, Joyce Whang

Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue (2022.findings-acl)

Challenge:	Existing retrieval-augmented language models assume query relevance and irrelevance as dichotomy . existing models are highly brittle to the presence of conflicting information in both the fine-tuning and in-context few-shot learning scenarios.
Approach:	They propose methods for handling knowledge conflicts by fine-tuning a discriminator or prompting it to elicit its discriminative capability.
Outcome:	The proposed approaches significantly enhance model robustness on open-domain QA.

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Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen

Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)

Challenge:	Multi-SentAugment and LayerAgg are self-training methods that augment available training data with similar (automatically labelled) in-domain sentences from large monolingual Web-scale corpora.
Approach:	They propose to use multi-sentaugment and layeragg to improve dialogue natural language understanding across multiple languages.
Outcome:	The proposed methods generalise well in zero- and few-shot scenarios and leverage external unannotated data sources.

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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)

Challenge:	Experimental results show that identifying the phases of opioid use disorder is highly contextual and challenging.
Approach:	They analyze 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use . they annotate span-level extractive explanations and critically evaluate state-of-the-art models in a supervised, few-shot, or zero-shot setting.
Outcome:	The proposed models improve classification accuracy and quality of the extracted explanations.

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Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, Jing Tang

Small Language Models Improve Giants by Rewriting Their Outputs (2024.eacl-long)

Challenge:	Existing LLMs are delicate and elusive in prompt words and styles.
Approach:	They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning .
Outcome:	The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality.

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Giorgos Vernikos, Arthur Brazinskas, Jakub Adamek, Jonathan Mallinson, Aliaksei Severyn, Eric Malmi

Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification (2024.findings-naacl)

Challenge:	despite impressive performance of large language models, they lag behind specialized models in various tasks.
Approach:	They propose a training model that can be integrated with different LLMs at inference to improve their performance without task-specific training.
Outcome:	The proposed model outperforms standard models on four natural language generation tasks.

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Robert Vacareanu, Fahmida Alam, Md Asiful Islam, Haris Riaz, Mihai Surdeanu

Evaluating Large Language Models via Linguistic Profiling (2024.emnlp-main)

Challenge:	a novel neuro-symbolic architecture for relation classification combines rule-based methods with deep learning techniques.
Approach:	They propose a neuro-symbolic architecture for relation classification that combines rule-based methods with deep learning techniques.
Outcome:	The proposed approach outperforms state-of-the-art models in three out of four settings . human interventions boost the performance on the relation org:parents by as much as 26% relative improvement .

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Alessio Miaschi, Felice Dell’Orletta, Giulia Venturi

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

Challenge:	Large Language Models (LLMs) undergo extensive evaluation against various benchmarks collected in established leaderboards to assess their performance across multiple tasks.
Approach:	They propose a new evaluation methodology to test LLMs' sentence generation abilities under specific linguistic constraints.
Outcome:	The proposed evaluation methodology is based on the 'linguistic profiling' approach and is not intended to be a task-oriented evaluation.

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Pride Kavumba, Ryo Takahashi, Yusuke Oda

Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)

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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Bang Yang, Fenglin Liu, Zheng Li, Qingyu Yin, Chenyu You, Bing Yin, Yuexian Zou

Large Language Models are Built-in Autoregressive Search Engines (2023.findings-acl)

Challenge:	Existing approaches to generate informative titles for products with limited labels are inadequate for novel products.
Approach:	They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products.
Outcome:	The proposed approach achieves state-of-the-art results on novel product categories with limited labels.

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Noah Ziems, Wenhao Yu, Zhihan Zhang, Meng Jiang

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning (2022.findings-emnlp)

Challenge:	Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing only shallow interactions between them.
Approach:	They propose to use large language models to generate URLs for document retrieval by following human instructions.
Outcome:	The proposed method achieves better retrieval performance than existing retrieval approaches on open-domain question answering benchmarks.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)

Challenge:	Existing approaches to learning KG triplets ignore ternary propagation patterns and ignore zero-shot, few-shot and synonymity problems.
Approach:	They propose a framework for contrastive learning based on ternary propagation patterns among head, relation and tail.
Outcome:	Experiments on benchmarks show that TernaryCL is superior to state-of-the-art models.

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Chaitanya Malaviya, Subin Lee, Dan Roth, Mark Yatskar

StoryWars: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation (2023.acl-long)

Challenge:	Question answering models can often be black boxes, as their reasoning process is mostly opaque.
Approach:	They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers.
Outcome:	The proposed model can be used to improve model responses by removing feedback from end users and enhancing model outputs by using natural language feedback.

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Yulun Du, Lydia Chilton

Memory, Show the Way: Memory Based Few Shot Word Representation Learning (D18-1)

Challenge:	Understanding and generating collaborative stories remains an underexplored area due to the lack of open-domain corpora.
Approach:	They propose to use a dataset of 40,000 collaborative stories written by 9,400 different authors from an online platform to generate a multi-task benchmark.
Outcome:	The proposed model achieves the best performance on fully-supervised, few-shot, and zero-shot scenarios while achieving the best results on the fully-supervised tasks.

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Jingyuan Sun, Shaonan Wang, Chengqing Zong

X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)

Challenge:	Existing word embedding methods for distributed semantic models require limited examples to learn a high quality representation.
Approach:	They propose a memory-based embedding learning method capable of acquiring word representations from limited context.
Outcome:	The proposed method delivers impressive performance on two challenging few-shot word similarity tasks.

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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam

Assessing Out-of-Domain Language Model Performance from Few Examples (2023.eacl-main)

Challenge:	X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research .
Approach:	They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source .
Outcome:	The proposed toolkit accelerates the post-editing of a new language dataset after translation.

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Prasann Singhal, Jarad Forristal, Xi Ye, Greg Durrett

GRAMMAR-LLM: Grammar-Constrained Natural Language Generation (2025.findings-acl)

Challenge:	Pretrained language models exhibit impressive generalization capabilities, but behave unpredictably under certain domain shifts.
Approach:	They propose to incorporate attributions into a few-shot model predicting out-of-domain (OOD) performance task to find out if models agree with pathological heuristics that may indicate worse generalization capabilities.
Outcome:	The proposed model-based model-learning model can perform better on a few-shot example set, and incorporate feature attributions to improve it.

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Gabriele Tuccio, Luana Bulla, Maria Madonia, Aldo Gangemi, Misael Mongiovì

Few-Shot Novel Concept Learning for Semantic Parsing (2021.findings-emnlp)

Challenge:	Existing approaches to fine-tuning and prompting are insufficient to ensure compliance with predefined taxonomies, syntactic structures, or domain-specific rules.
Approach:	They propose a framework that integrates formal grammatical constraints into the decoding process to enforce syntactic correctness in linear time while maintaining expressiveness in grammar rule definition.
Outcome:	The proposed framework enforces syntactic correctness in linear time while maintaining expressiveness in grammar rule definition.

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Soham Dan, Osbert Bastani, Dan Roth

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching (2020.coling-main)

Challenge:	Existing deep learning algorithms typically require thousands of examples to learn novel concepts.
Approach:	They propose an algorithm for learning novel concepts by representing them as programs over existing concepts.
Outcome:	The proposed approach outperforms end-to-end neural semantic parsers in a few-shot novel concept learning setting.

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Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition (2024.findings-emnlp)

Challenge:	Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data.
Approach:	They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve table’s structural information.
Outcome:	The proposed model outperforms existing systems on most few-shot settings.

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Wei Chen, Lili Zhao, Zhi Zheng, Tong Xu, Yang Wang, Enhong Chen

Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension (2023.findings-eacl)

Challenge:	Recent studies have demonstrated remarkable performance on few-shot Named Entity Recognition tasks due to the high cost of obtaining high-quality labeled data.
Approach:	They propose to decompose the task into entity span detection and entity type classification using a type-independent entity span detector and then classify the detected spans based on their types.
Outcome:	The proposed method consistently yields improvements over two baseline approaches.

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Chen Zhang, Yuxuan Lai, Yansong Feng, Xingyu Shen, Haowei Du, Dongyan Zhao

Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (2022.findings-naacl)

Challenge:	Existing high-quality xMRC datasets can be further utilized to fine-tune our model.
Approach:	They propose a cross-lingual question answering over knowledge base approach that converts KB subgraphs into passages to narrow the gap between KB schemas and questions.
Outcome:	The proposed approach outperforms baselines and achieves strong few-shot and zero-shot performance on two xKBQA datasets in 12 languages.

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Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, Eneko Agirre

PCBERT: Parent and Child BERT for Chinese Few-shot NER (2022.coling-1)

Challenge:	Recent work shows that Relation Extraction tasks can be recasted as Textual Entailment tasks using verbalizations.
Approach:	They propose to recasted RE tasks as Textual Entailment tasks using verbalizations . they show that entailment reduces the need for manual annotation to 50% and 20% .
Outcome:	The proposed method reduces the need for manual annotation to 50% and 20% in event argument extraction tasks while achieving the same performance as with full training.

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Peichao Lai, Feiyang Ye, Lin Zhang, Zhiwei Chen, Yanggeng Fu, Yingjie Wu, Yilei Wang

A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models (2022.acl-long)

Challenge:	Existing approaches to improve model performance on few-shot or zero-shot datasets are not effective for Chinese few- shot NER.
Approach:	They propose a prompt-based Parent and Child BERT for Chinese few-shot NER to train an annotating model on high-resource datasets and then discover more implicit labels on low-resourced datasets.
Outcome:	The proposed model can be used on Weibo and other Chinese NER datasets and it is shown to be effective in few-shot learning.

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Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, Xiang Ren

SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (2023.eacl-main)

Challenge:	Recent few-shot learning models such as GPT3 are expensive and slow to deploy for real-world applications.
Approach:	They propose a prompt-based low-resource learning method for VL tasks with a few examples . they pre-train a sequence-to-sequence transformer model with prefix and masked language modeling .
Outcome:	The proposed method outperforms Frozen on vision-language tasks with prompt-based learning by 18.2% point.

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Koustava Goswami, Lukas Lange, Jun Araki, Heike Adel

Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (2023.findings-acl)

Challenge:	Recent work shows promising results when prompting pre-trained language models, but in low-resource domains, the domain gap between the pre-training data and the downstream task is too large.
Approach:	They propose a method for prompting pre-trained language models using domain-specific keywords with a trainable gated prompt.
Outcome:	The proposed prompting method outperforms state-of-the-art prompting methods on three text classification benchmarks and shows that it reduces the need for domain-specific language model pre-training.

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Shan Zhang, Bin Cao, Tianming Zhang, Yuqi Liu, Jing Fan

What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore label dependency, resulting in suboptimal performance.
Approach:	They propose a meta-learning method to make label dependency transferable by learning general initialization and individual parameter update rule for label dependency.
Outcome:	The proposed method improves existing methods by learning general initialization and individual parameter update rule for label dependency.

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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain

Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (2021.findings-acl)

Challenge:	Existing research shows that large language models can perform better in machine translation tasks.
Approach:	They propose to use large language models for machine translation evaluations . authors explore what translation information is needed for LLMs to evaluate MT quality .
Outcome:	The proposed model performs comparable to fine-tuned multilingual pre-trained models.

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Shirong Shen, Tongtong Wu, Guilin Qi, Yuan-Fang Li, Gholamreza Haffari, Sheng Bi

Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)

Challenge:	Event detection typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem.
Approach:	They propose a knowledge-based fewshot event detection method which introduces external event knowledge as the knowledge prior of event types.
Outcome:	Experiments show that the proposed method outperforms baselines by 15 F 1 points . event detection is an important task in information extraction .

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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk

CPL: Counterfactual Prompt Learning for Vision and Language Models (2022.emnlp-main)

Challenge:	Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space.
Approach:	They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations.
Outcome:	The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering.

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Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Wang

Cross-Lingual Leveled Reading Based on Language-Invariant Features (2021.findings-emnlp)

Challenge:	Existing prompt tuning methods tend to learn spurious or entangled representations, leading to poor generalization to unseen concepts.
Approach:	They propose a prompt tuning technique that tunes the learnable prompt for pre-trained vision and language models.
Outcome:	The proposed method improves few-shot performance on vision and language tasks over existing prompt tuning methods.

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Simin Rao, Hua Zheng, Sujian Li

CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)

Challenge:	Leveled reading (LR) aims to automatically classify texts by the cognitive levels of readers.
Approach:	They propose to use adversarial training and cross-lingual pre-training methods to transfer LR knowledge from annotated data in resource-rich English to Chinese.
Outcome:	The proposed method captures language-invariant features between English and Chinese.

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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang

ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation (2023.findings-emnlp)

Challenge:	Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data.
Approach:	They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism.
Outcome:	The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model.

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Martin Vejvar, Yasutaka Fujimoto

Natural Language to Code Translation with Execution (2022.emnlp-main)

Challenge:	Unlike previous methods, large language models produce entity-agnostic templates instead of copying the given example entities or validating/crafting the templates manually.
Approach:	They propose an approach for structured data verbalisation into short template sentences in zero to few-shot settings that prompts Large Language Models to directly produce entity-agnostic templates.
Outcome:	The proposed approach averages 66% parsing error rate reduction in generated verbalisations of RDF triples on the DART dataset.

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Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, Sida I. Wang

TS-CLIP: Time Series Understanding by CLIP (2025.emnlp-main)

Challenge:	Generative code models do not explicitly incorporate program semantics during training, but they are able to generate correct solutions for many problems.
Approach:	They introduce execution result-based minimum Bayes risk decoding for program selection . they select output programs from a generated candidate set by marginalizing over implementations that share the same semantics .
Outcome:	The proposed model outperforms all other methods on natural language-to-code translation.

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Ziwen Chen, Xiaoyuan Zhang, Ming Zhu

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

Challenge:	Contrastive Language–Image Pre-training (CLIP) has demonstrated remarkable success in aligning vision and language.
Approach:	They propose a synonym bank mechanism that generates synonym embeddings as alignment targets.
Outcome:	The proposed approach achieves state-of-the-art (SOTA) performance on 51 datasets.

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Zihan Wang, Kewen Zhao, Zilong Wang, Jingbo Shang

Prompting Few-shot Multi-hop Question Generation via Comprehending Type-aware Semantics (2024.findings-naacl)

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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Zefeng Lin, Weidong Chen, Yan Song, Yongdong Zhang

Characterizing Human and Zero-Shot GPT-3.5 Object-Similarity Judgments (2024.findings-naacl)

Challenge:	Existing approaches for multi-hop question generation rely on large annotated data . supervised approaches rely only on large labeled data, making it hard to perform tasks.
Approach:	They propose a type-aware semantics extraction-based chain-of-thought method for multi-hop question generation for documents . they first extract question types and essential semantic phrases from the given documents and the answer .
Outcome:	The proposed approach extracts question types and essential semantic phrases from documents and the answer.

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D McKnight, Alona Fyshe

Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification (2021.findings-acl)

Challenge:	Recent advances in large language models have yielded few-shot, human-comparable performance on a range of tasks, but studies of LLM annotation accuracy and behavior are sparse.
Approach:	They characterize OpenAI’s GPT-3.5’s judgment on a behavioral task for implicit object categorization and give similarities and differences between them.
Outcome:	The proposed model augments human responses with LLMs for domains where data is sparse or compute resources are low.

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Qiaoyang Luo, Lingqiao Liu, Yuhao Lin, Wei Zhang

FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4 (2023.findings-emnlp)

Challenge:	Existing studies focus on building a meta-learner from input text but ignore abundant semantic information beneath class labels.
Approach:	They propose a framework to make full use of label semantics in few-shot text classification systems.
Outcome:	The proposed framework can be plugged into the existing few-shot text classification system.

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Jeonghwan Kim, Giwon Hong, Sung-Hyon Myaeng, Joyce Whang

Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)

Challenge:	Large language models such as GPT-4 have demonstrated impressive capability to solve textual understanding problems at a level parallel to or surpassing state-of-the-art taskspecific models.
Approach:	They propose to transfer task-specific inductive biases from finetuned models to prompts to improve GPT-4's compositional reasoning capabilities.
Outcome:	The proposed prompt scheme shows competitive zero-shot and few-shot performances compared to existing prompts on complicated reasoning tasks.

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Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, Zhiyuan Liu

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression (2021.emnlp-main)

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.

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Chenhe Dong, Yaliang Li, Ying Shen, Minghui Qiu

Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction (2025.naacl-long)

Challenge:	Large pre-trained language models (PLMs) have shown overwhelming performances on many tasks, but their large size and slow inference speed have hindered practical deployments.
Approach:	They propose a hierarchical relational knowledge distillation method to capture hierarchic and domain relational information.
Outcome:	The proposed method outperforms existing methods on multi-domain datasets and is highly reproducible.

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Wei Li, Wen Luo, Guangyue Peng, Houfeng Wang

A Unified Supervised and Unsupervised Dialogue Topic Segmentation Framework Based on Utterance Pair Modeling (2025.naacl-long)

Challenge:	Grammatical error correction (GEC) aims to correct grammatical, spelling, and semantic errors in natural language text.
Approach:	They propose a retrieval method based on natural language grammatical error explanations to match inputs with pre-constructed databases where explanations for erroneous samples are generated by LLMs.
Outcome:	The proposed method outperforms existing semantic and BM25-based retrieval techniques without additional training or language adaptation.

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Shihao Yang, Ziyi Zhang, Yue Jiang, Chunsheng Qin, Shuhua Liu

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)

Challenge:	Unsupervised methods for dialogue topic segmentation are difficult to surpass due to short sentences, serious references and non-standard language.
Approach:	They propose a method to divide a dialogue into different topic paragraphs to better understand its structure and content.
Outcome:	The proposed method achieves the best results on multiple benchmark datasets across different scenarios.

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Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis (2022.naacl-main)

Challenge:	Existing language models only touch nouns or verbs within simplified events or specific domains.
Approach:	They propose an entailment graph that collects abstract knowledge for 3 components of diverse events to comprehensively evaluate the abstraction ability of language models.
Outcome:	The proposed benchmark improves LLMs across two previous abstraction tasks.

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Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup (2022.findings-emnlp)

Challenge:	Currently, global models are not able to produce personalized responses for individual users, based on their data.
Approach:	They propose a scheme for training a single shared model for all users by prepending a fixed, user-specific non-trainable string to each user’s input text.
Outcome:	The proposed method outperforms the state-of-the-art model on a suite of sentiment analysis datasets by up to 13 points.

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Yordan Yordanov, Vid Kocijan, Thomas Lukasiewicz, Oana-Maria Camburu

BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)

Challenge:	Existing approaches to train models to provide natural language explanations (NLEs) require acquisition of task-specific NLEs, which is time- and resource-consuming.
Approach:	They propose a few-shot out-of-domain transfer of NLEs from a parent task to a child task . they propose four methods that cover possible fine-tuning combinations of NLESs and labels .
Outcome:	The proposed methods cover the possible fine-tuning combinations of labels and NLEs for the parent and child tasks.

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Tianxiang Sun, Zhengfu He, Hong Qian, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

On Measuring the Intrinsic Few-Shot Hardness of Datasets (2022.emnlp-main)

Challenge:	Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged.
Approach:	They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately.
Outcome:	The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters.

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Xinran Zhao, Shikhar Murty, Christopher Manning

InstructExcel: A Benchmark for Natural Language Instruction in Excel (2023.findings-emnlp)

Challenge:	Recent work has shown that few-shot learning is successful for pre-trained models, but there is no concrete understanding of when and why it is successful.
Approach:	They propose a simple metric that estimates few-shot hardness for a given dataset . they propose metric which exploits feature-space invariances between training and test samples .
Outcome:	The proposed metric better accounts for few-shot hardness compared to existing notions and is 8-100x faster to compute.

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Justin Payan, Swaroop Mishra, Mukul Singh, Carina Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, Elnaz Nouri

OASum: Large-Scale Open Domain Aspect-based Summarization (2023.findings-acl)

Challenge:	Large Language Models (LLMs) can solve increasingly complex NLP tasks such as Excel specific tasks.
Approach:	They propose a large-scale benchmark to test whether Large Language Models can generate code that solves Excel specific tasks provided via natural language user instructions.
Outcome:	The proposed model outperforms existing models and provides a hard benchmark for state of the art models like GPT-4.

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Xianjun Yang, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Xiaoman Pan, Linda Petzold, Dong Yu

Summarization

Challenge:	Existing generic summarization methods generate only one summary for all different requests which is not optimal for diverse demands.
Approach:	They use crowd-sourced knowledge on Wikipedia to create a large-scale open-domain aspect-based summarization dataset with 1 million different aspects on 2 million Wikipedia pages.
Outcome:	The proposed model can generate diverse aspect-based summarizations on Wikipedia with zero/few-shot and fine-tuning on seven downstream datasets.

Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)

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Michael Ryan, Tarek Naous, Wei Xu

Paraphrasing

Challenge:	Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating.
Approach:	They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages.
Outcome:	The proposed benchmark outperforms pre-trained models in Russian in zero-shot cross-lingual transfer to low-resource languages.

Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments (2023.acl-long)

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Yu Gu, Xiang Deng, Yu Su

X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification (2024.findings-acl)

Challenge:	Existing language models lack grounding to real-world environments . a missing piece is the connection between LMs and the environment .
Approach:	They propose a generic framework for grounded language understanding that capitalizes on discriminative ability of LMs instead of their generative ability.
Outcome:	The proposed framework capitalizes on discriminative ability of LMs instead of their generative ability.

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Hanzi Xu, Muhao Chen, Lifu Huang, Slobodan Vucetic, Wenpeng Yin

Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

Challenge:	Recent studies have focused on few-shot and zero-shot learning, but label occurrences vary widely . authors propose a new classification challenge that can be used to manage labels across the full frequency spectrum .
Approach:	They propose a new classification challenge that allows for label co-occurrences without predefined limits.
Outcome:	The proposed system can handle freq-shot, few-shot and zero-shot labels without limits.

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Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, Lei Li

Structured Data in NLP

Challenge:	Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach:	They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome:	The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.

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

Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)

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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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang

BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting (2025.findings-naacl)

Challenge:	Z-Code++ is a pre-trained language model optimized for abstractive text summarization.
Approach:	They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance.
Outcome:	The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings.

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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

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)

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.

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Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)

Challenge:	Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks .
Approach:	They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain.
Outcome:	The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings.

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Lei Zhao, Cheng Yao

Chitchat as Interference: Adding User Backstories to Task-Oriented Dialogues (2024.lrec-main)

Challenge:	Existing methods for few-shot text classification are limited by labeled data.
Approach:	They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views.
Outcome:	The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer.

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Armand Stricker, Patrick Paroubek

The “r” in “woman” stands for rights. Auditing LLMs in Uncovering Social Dynamics in Implicit Misogyny (2025.findings-emnlp)

Challenge:	During task-oriented dialogues, human users naturally introduce chitchat that is beyond the immediate scope of the task, interfering with the flow of the conversation.
Approach:	They use few-shot prompting to augment a multi-task chitchat dataset with user backstories to address this issue without the need for expensive manual data creation.
Outcome:	The proposed model can be used for training and can move the task forward in the same turn, as confirmed by human evaluation.

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Arianna Muti, Chris Emmery, Debora Nozza, Alberto Barrón-Cedeño, Tommaso Caselli

Challenge:	a recent study examined misogynistic expressions in English and Italian . a taxonomy of social dynamics is used to identify misogorical expressions .
Approach:	They examine misogynistic expressions in English and Italian using a taxonomy of social dynamics . they find that LLMs struggle to follow instructions and reason in all settings .
Outcome:	The results show that misogynistic expressions are more often implicit than openly hostile . the authors show that LLMs struggle to follow instructions and reason in all settings .

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

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

Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization (2024.findings-acl)

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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Zijian Lei, Dong Qian, William Cheung

ZARA: Improving Few-Shot Self-Rationalization for Small Language Models (2023.findings-emnlp)

Challenge:	Low-rank adaptation (LoRA) is an efficient way to fine-tune large language models (LLMs) but its memory overhead restricts it to scale up when the model size increases.
Approach:	They propose a low-rank adaptation method which decomposes model weight updates into a pair of low-ranked projection matrices and incorporates a regularizer to improve generalization.
Outcome:	The proposed method achieves better performance under few-shot settings and similar performance to the SOTA low-rank adaptation methods.

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Wei-Lin Chen, An-Zi Yen, Cheng-Kuang Wu, Hen-Hsen Huang, Hsin-Hsi Chen

DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning (2023.emnlp-main)

Challenge:	Recent studies demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars.
Approach:	They propose to leverage explanations for small LMs to improve few-shot self-rationalization by reducing the problem of plausibility judgement to natural language inference.
Outcome:	The proposed approach achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric.

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Praveen Venkateswaran, Evelyn Duesterwald, Vatche Isahagian

Node Placement in Argument Maps: Modeling Unidirectional Relations in High & Low-Resource Scenarios (2023.acl-long)

Challenge:	Existing approaches to task-oriented conversation system DST use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models and elicit slot values from the dialogue context.
Approach:	They propose a generalizable in-context tuning approach that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates.
Outcome:	Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings using a much smaller model.

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Iman Jundi, Neele Falk, Eva Maria Vecchi, Gabriella Lapesa

Drift: Decoding-time Personalized Alignments with Implicit User Preferences (2025.findings-emnlp)

Challenge:	Argument maps structure discourse into nodes with each node being an argument that supports or opposes its parent argument.
Approach:	They propose a task of node placement: suggesting candidate nodes as parents for a new contribution.
Outcome:	The proposed method improves the quality of the argument maps and reduces redundancy.

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Minbeom Kim, Kang-il Lee, Seongho Joo, Hwaran Lee, Thibaut Thonet, Kyomin Jung

Exploring Chain of Thought Style Prompting for Text-to-SQL (2023.emnlp-main)

Challenge:	Drift personalizes large language models at decoding time with implicit user preferences . Unlike traditional Reinforcement Learning from Human Feedback, Drift operates in a training-free manner .
Approach:	They propose a framework that personalizes large language models at decoding time with implicit user preferences.
Outcome:	The proposed framework personalizes large language models at decoding time with implicit user preferences.

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Chang-Yu Tai, Ziru Chen, Tianshu Zhang, Xiang Deng, Huan Sun

Programming Languages in NLP

Challenge:	In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks.
Approach:	They propose a new chain of thought prompting method that enhances LLMs’ reasoning ability through chain of thinking prompting, including the original chain-of-thought prompting and least-to-most prompting.
Outcome:	The proposed method brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gain, versus the least-to-most prompting.

Thinking about how to extract: Energizing LLMs’ emergence capabilities for document-level event argument extraction (2024.findings-acl)

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Kai Shuang, Zhouji Zhouji, Wang Qiwei, Jinyu Guo

Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)

Challenge:	Existing models for document-level event argument extraction (D-EAE) lack key feature forgetting and cross-event argument confusion.
Approach:	They propose a document-level event argument extraction method based on guided summarization and reasoning that leverages the emergence capabilities of large language models to highlight key event information.
Outcome:	The proposed method outperforms baseline models by 1.3% F1 and 1.6% F1 on WIKIEVENTS and RAMS.

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Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, Xiaodong He

Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022.findings-emnlp)

Challenge:	Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks.
Approach:	They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese.
Outcome:	The proposed model improves the few-shot table understanding in Chinese.

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Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, You Chen, Lang Li, Huan Sun, Yu Su

Testing the Ability of Language Models to Interpret Figurative Language (2022.naacl-main)

Challenge:	Large pre-trained language models (PLMs) such as GPT-3 have shown strong in-context learning capabilities, which are appealing for domains such as biomedicine that feature high and diverse demands of language technologies but also high data annotation costs.
Approach:	They propose to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two representative biomedical information extraction tasks: named entity recognition and relation extraction.
Outcome:	The proposed model underperforms on two representative biomedical information extraction tasks.

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Emmy Liu, Chenxuan Cui, Kenneth Zheng, Graham Neubig

Word Frequency Does Not Predict Grammatical Knowledge in Language Models (2020.emnlp-main)

Challenge:	Existing work on figurative language has not been done on literal language models.
Approach:	They propose a Winograd-style task to evaluate figurative phrases with divergent meanings by interpreting paired figurativ phrases with a human input.
Outcome:	The proposed task outperforms state-of-the-art models on a nonliteral language understanding task in zero-shot settings.

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Charles Yu, Ryan Sie, Nicolas Tedeschi, Leon Bergen

The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities (2024.acl-long)

Challenge:	Neural language models learn the grammatical properties of natural languages to varying degrees of accuracy.
Approach:	They focus on subject-verb agreement and reflexive anaphora to investigate whether there are systematic sources of variation in the language models’ accuracy.
Outcome:	The proposed model can learn grammatical properties from training data.

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David Stap, Eva Hasler, Bill Byrne, Christof Monz, Ke Tran

HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models (2024.findings-emnlp)

Challenge:	Recent studies have shown that fine-tuning large language models improves their translations, but it is unclear what is the impact on desirable LLM behaviors that are not present in neural machine translation models.
Approach:	They perform an extensive translation evaluation on LLaMA and Falcon models with model size ranging from 7 billion up to 65 billion parameters.
Outcome:	The proposed model produces less literal translations after fine-tuning on parallel data.

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Huy Nghiem, Hal Daumé Iii

Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking (2023.findings-acl)

Challenge:	Social media has amplified the propagation of hateful sentiments, highlighting the contested nature of "offensive content" research shows that "of offensive content" is still a contested construct due to varying definitions and labeling.
Approach:	They propose a dataset that features human-curated explanations for offensive content in English . they show that HateCOT pretraining improves performance of open-source LLMs .
Outcome:	The proposed model improves on three benchmark datasets for offensive content detection . the model improve the quality of its explanations, as confirmed by the human evaluation .

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Brendan King, Jeffrey Flanigan

Realistic Training Data Generation and Rule Enhanced Decoding in LLM for NameGuess (2025.emnlp-main)

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.

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Yikuan Xia, Jiazun Chen, Sujian Li, Jun Gao

ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification (2025.findings-naacl)

Challenge:	Abbreviated column names often harm downstream tasks, causing performance drops of 10.54, 40.50, and 3.83 percentage points.
Approach:	They propose a method that integrates a subsequence abbreviation generator trained on human-annotated data and collects non-subsequent abbrevations to improve the training set.
Outcome:	The proposed approach improves on the English NameGuess task and surpasses state-of-the-art LLMs.

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Yaswanth M, Vaibhav Singh, Ayush Maheshwari, Amrith Krishna, Ganesh Ramakrishnan

Focused Large Language Models are Stable Many-Shot Learners (2024.emnlp-main)

Challenge:	Existing frameworks for large language models (LLMs) generate high-quality synthetic data that can be used to supplement training data or surpass crowd-sourced annotations.
Approach:	They propose a framework that iteratively induces rules and generates synthetic data for text classification.
Outcome:	The proposed framework outperforms existing models on in-context learning and fine-tuning settings by using augmented data.

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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Heda Wang, Yao Hu, Kan Li

PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (2022.acl-long)

Challenge:	In-Context Learning (ICL) enables large language models to achieve rapid task adaptation by learning from demonstrations.
Approach:	They propose a training-free method that disperses model attention from the query . they propose 'focus' search strategy that uses model perplexity to ensure sufficient attention .
Outcome:	The proposed method achieves an average performance improvement of 5.2% over vanilla ICL and scales well with many-shot demonstrations.

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Wen Xiao, Iz Beltagy, Giuseppe Carenini, Arman Cohan

OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)

Challenge:	Existing pretrained models require domain-specific additional information to be effective.
Approach:	They propose a pre-trained model for multi-document representation with a focus on summarization that uses efficient encoder-decoder transformers to simplify the processing of concatenated input documents.
Outcome:	PRIMERA outperforms current state-of-the-art models on most datasets with large margins . PRImerA uses efficient encoder-decoder transformers to simplify processing of concatenated input documents.

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Xize Cheng, Tao Jin, Linjun Li, Wang Lin, Xinyu Duan, Zhou Zhao

Prompting Language Models for Linguistic Structure (2023.acl-long)

Challenge:	Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech.
Approach:	They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment .
Outcome:	The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively.

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Terra Blevins, Hila Gonen, Luke Zettlemoyer

Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish (2024.lrec-main)

Challenge:	Existing prompting methods can test this hypothesis on autoregressive PLMs.
Approach:	They propose a structured prompting approach for linguistic structured prediction tasks that performs zero- and few-shot sequence tagging with autoregressive PLMs.
Outcome:	The proposed approach shows that the model can perform few-shot sequence tagging on part-of-speech taging, named entity recognition, and sentence chunking tasks.

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Recep Firat Cekinel, Çağrı Çöltekin, Pinar Karagoz

Aligning Black-box Language Models with Human Judgments (2025.findings-naacl)

Challenge:	Currently, most of the research on misinformation is focused on the English language . however, there is a scarcity of datasets for other languages, including Turkish .
Approach:	They propose a dataset that spans multiple domains and incorporates evidence from three Turkish fact-checking organizations.
Outcome:	The proposed dataset has the potential to advance research in the Turkish language.

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Gerrit J.j. Van Den Burg, Gen Suzuki, Wei Liu, Murat Sensoy

On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model (2022.naacl-main)

Challenge:	Large language models (LLMs) are increasingly used as automated judges to evaluate recommendation systems, search engines, and other subjective tasks.
Approach:	They propose a framework to align LLM judgments with individual human evaluators or their aggregated judgments without retraining or fine-tuning the LLM.
Outcome:	The proposed framework achieves 142% improvement in agreement across 29 tasks and exceeds inter-human agreement on four out of six tasks.

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Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, Woomyoung Park, Jung-Woo Ha, Nako Sung

Better Synthetic Data by Retrieving and Transforming Existing Datasets (2024.findings-acl)

Challenge:	Recent studies on large-scale in-context language models have reported successful in-const zero- and few-shot learning ability.
Approach:	They investigate the effects of the pretraining corpus on in-context learning in a Korean-centric model.
Outcome:	The study shows that pretraining corpus size does not determine in-context learning ability . the findings suggest that in-constext learning is not always competitive .

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Saumya Gandhi, Ritu Gala, Vijay Viswanathan, Tongshuang Wu, Graham Neubig

Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (2021.acl-long)

Challenge:	despite advances in large language models, task-specific data is not available for many use cases . a new method to improve automated dataset generation uses publicly available datasets .
Approach:	They propose a method to make better use of existing datasets to improve automatic dataset generation.
Outcome:	The proposed method outperforms existing methods on language-based tasks . it significantly increases diversity and difficulty of generated data on many tasks compared to other methods .

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Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett

Unified Structure Generation for Universal Information Extraction (2022.acl-long)

Challenge:	Neural information retrieval models have shown advanced results in many ranking scenarios where massive relevance labels or clickthrough data are available.
Approach:	They propose a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains.
Outcome:	The proposed method improves the few-shot ranking accuracy of Neu-IR models on three TREC benchmarks in the web, news, and biomedical domains.

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Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu

Data-Efficient Language Shaped Few-shot Image Classification (2021.findings-emnlp)

Challenge:	Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Approach:	They propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources.
Outcome:	The proposed framework can model different IE tasks, generate targeted structures, and learn general IE abilities from different knowledge sources.

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Zhenwen Liang, Xiangliang Zhang

Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)

Challenge:	Existing studies have shown that language is helpful guider for image understanding by neural networks.
Approach:	They propose a language-shaped learning method that makes the best use of the few-shot images and the language available only in training.
Outcome:	The proposed method outperforms state-of-the-art methods on a few-shot dataset with limited training data.

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Apoorva Singh, Raghav Jain, Prince Jha, Sriparna Saha

Explainability & Interpretability in NLP

Challenge:	Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations.
Approach:	They propose to use an explainable complaint dataset to generate a commonsense-aware generative framework that can predict the complaint cause, severity level, emotion, and polarity of the text.
Outcome:	The proposed model can predict the complaint cause, severity level, emotion, and polarity of the text in addition to detecting whether it is a complaint or not.

Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)

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Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler

Consistent Prototype Learning for Few-Shot Continual Relation Extraction (2023.acl-long)

Challenge:	Named entity recognition is a technique for extracting information from unstructured data with known labels.
Approach:	They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases .
Outcome:	The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods .

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Xiudi Chen, Hui Wu, Xiaodong Shi

Long Document Ranking with Query-Directed Sparse Transformer (2020.findings-emnlp)

Challenge:	Existing methods for few-shot continual relation extraction are overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle confusion of similar classes.
Approach:	They propose a few-shot continual relation extraction task that uses memory-enhanced modules to train a model on incrementally few-shot data to avoid forgetting old relations.
Outcome:	The proposed method outperforms existing methods on two commonly-used datasets.

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Jyun-Yu Jiang, Chenyan Xiong, Chia-Jung Lee, Wei Wang

CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)

Challenge:	Existing approaches to document ranking require long documents to be broken to fit in pretrained models.
Approach:	They propose a Query-Directed Sparse attention model that induces IR-axiomatic structures in transformer self-attention.
Outcome:	The proposed model enforces the principle properties desired in ranking while also enjoying efficiency from sparsity.

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Haoyu Song, Li Dong, Weinan Zhang, Ting Liu, Furu Wei

Visual Data in NLP

Challenge:	Previously, CLIP was only regarded as a powerful visual encoder.
Approach:	They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure.
Outcome:	The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure.

Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)

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Huiming Wang, Liying Cheng, Wenxuan Zhang, De Wen Soh, Lidong Bing

Towards Realistic Few-Shot Relation Extraction (2021.emnlp-main)

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.

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Sam Brody, Sichao Wu, Adrian Benton

Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning (2023.acl-long)

Challenge:	Recent studies have shown that few-shot relation classification models can be used to extract any relation of interest from a collection of text with only a few example instances.
Approach:	They propose to modify the training routine to encourage models to better discriminate between relations involving similar entity types.
Outcome:	The proposed models outperform human models on relation extraction tasks while relying on entity type information.

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Zhenrui Yue, Huimin Zeng, Mengfei Lan, Heng Ji, Dong Wang

Causal Document-Grounded Dialogue Pre-training (2023.emnlp-main)

Challenge:	Existing methods for event detection often fail to detect unseen or rare events due to the lack of domain knowledge.
Approach:	They propose a meta learning-based framework for zero-shot event detection that uses a prompt-based prompt and a trigger-aware soft verbalizer to efficiently project output to unseen tasks.
Outcome:	The proposed framework performs state-of-the-art in zero-shot and few-shot scenarios on benchmark datasets FewEvent and MAVEN.

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Yingxiu Zhao, Bowen Yu, Bowen Li, Haiyang Yu, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin Zhang

A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters (2021.acl-long)

Challenge:	Existing methods for document-grounded dialogue (DocGD) rely on general pre-trained language models without a tailored pre-training approach that explicitly captures causal relationships.
Approach:	They propose a causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora and a perturbation-based strategy to capture causality.
Outcome:	The proposed strategy yields significant and consistent improvements in fully-supervised, low-resource, few-shot, and zero-shot settings.

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Mengjie Zhao, Yi Zhu, Ehsan Shareghi, Ivan Vulić, Roi Reichart, Anna Korhonen, Hinrich Schütze

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering (2025.findings-emnlp)

Challenge:	Few-shot crosslingual transfer outperforms zero-shot with pretrained encoders like multilingual BERT.
Approach:	They conduct an experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages.
Outcome:	The proposed model outperforms state-of-the-art approaches on lexical features and a full model finetuning approach outperformed several state- of-the art approaches.

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Patrick Sutanto, Joan Santoso, Esther Irawati Setiawan, Aji Prasetya Wibawa

Boosting Natural Language Generation from Instructions with Meta-Learning (2022.emnlp-main)

Challenge:	Large Language Models excel at few-shot learning but their direct application in real-world scenarios is often hindered by their high computational cost.
Approach:	They propose a framework that uses Large Language Models for data generation and scoring to improve encoder model performance.
Outcome:	The proposed approach improves accuracy from 28.9% to 39.3% on a few-shot MCQA task .

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Budhaditya Deb, Ahmed Hassan Awadallah, Guoqing Zheng

Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization (2023.findings-emnlp)

Challenge:	Recent work shows that language models trained with multi-task instructional learning (MTIL) can solve diverse NLP tasks in zero-shot settings with improved performance compared to prompt tuning.
Approach:	They propose to adapt meta-learning to MTIL in three directions: 1) Model Agnostic Meta Learning (MAML), 2) Hyper-Network adaptation to generate task specific parameters conditioned on instructions.
Outcome:	The proposed approaches improve over strong baselines in zero-shot settings and are most impactful when the test tasks are strictly zero- shot and are "hard"

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Weixiao Zhou, Gengyao Li, Xianfu Cheng, Xinnian Liang, Junnan Zhu, Feifei Zhai, Zhoujun Li

Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)

Challenge:	Existing methods for dialogue summarization only apply to specific scenarios and domains.
Approach:	They propose a pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization.
Outcome:	The proposed model significantly outperforms state-of-the-art models on three dialogue summarization datasets from different scenarios and domains.

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Yinhao Bai, Zhixin Han, Yuhua Zhao, Hang Gao, Zhuowei Zhang, Xunzhi Wang, Mengting Hu

ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)

Challenge:	Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions .
Approach:	They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions .
Outcome:	The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas.

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Jinxin Liu, Jiaxin Shi, Ji Qi, Lei Hou, Juanzi Li, Qi Tian

Universal Self-Adaptive Prompting (2023.emnlp-main)

Challenge:	Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity.
Approach:	They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity.
Outcome:	The proposed framework achieves 9.1% and 3.3% absolute gains over previous SOTA on Quora and MSCOCO and can improve to 18.0% and 4.6% on GLUE.

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Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Arik, Tomas Pfister

Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning (2025.findings-naacl)

Challenge:	a hallmark of modern large language models is their impressive general zero-shot and few-shot abilities . however, zero- shot performances are weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks.
Approach:	They propose an automatic prompt design approach specifically tailored for zero-shot learning that categorizes a possible NLP task into one of three possible task types and then uses a selector to select the most suitable queries and zero- shot model-generated responses as pseudo-demonstrations.
Outcome:	The proposed approach is able to generalize ICL to zero-shot learning tasks while also allowing for a more efficient and efficient prompt design.

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Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych, Alham Fikri Aji

EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot settings, but they struggle with extending to few- shot and zero- shot settings due to their architectural design.
Approach:	They propose a technique that models discriminative tasks as a set of finite statements and trains an encoder model to discriminate between the potential statements to determine the label.
Outcome:	The proposed method achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters.

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Haozhe Chen, Run Chen, Julia Hirschberg

Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue (2024.findings-emnlp)

Challenge:	EmoKnob framework allows fine-grained emotion control in speech synthesis with few-shot demonstrative samples of arbitrary emotion.
Approach:	They propose a framework that allows fine-grained emotion control in speech synthesis . they propose two methods to apply emotion control on emotions described by open-ended text .
Outcome:	The proposed framework allows fine-grained emotion control in speech synthesis with few-shot demonstrative samples of arbitrary emotion.

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Huifang Du, Shuqin Li, Minghao Wu, Xuejing Feng, Yuan-Fang Li, Haofen Wang

Position Really Matters: Towards a Holistic Approach for Prompt Tuning (2025.findings-naacl)

Challenge:	Existing RL methods focus on generation tasks while neglecting dialogue state tracking (DST) for understanding.
Approach:	They propose a method that integrates RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation.
Outcome:	The proposed approach achieves state-of-the-art results on three widely used datasets.

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Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Ruth Petzold, Haifeng Chen

Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning (2025.coling-main)

Challenge:	Prompt tuning is effective in extracting knowledge from foundation models, but its effectiveness is uncertain.
Approach:	They propose a parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances.
Outcome:	The proposed approach improves performance across a wide range of tasks including NLP, vision recognition, and vision-language tasks.

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Chaoqun Cui, Caiyan Jia

Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment (2023.findings-emnlp)

Challenge:	Existing methods for rumor detection are limited in labeled data, but social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts.
Approach:	They propose a framework for rumor detection with Graph Supervised Contrastive Learning that heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumors detection.
Outcome:	The proposed framework heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumor detection.

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Zhen Zhang, Jialu Wang, Xin Wang

Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models (2024.acl-long)

Challenge:	Existing cross-lingual transfer methods that use labeled data and linguistic resources would consume excessive resources for a large number of languages.
Approach:	They propose a parameter-efficient cross-lingual transfer learning framework that utilizes a translation-based alignment method to mitigate multilingual disparities.
Outcome:	The proposed framework reduces disparities among languages and improves cross-lingual transfer results in low-resource scenarios while keeping and fine-tuning only a small number of parameters.

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Tianjie Ju, Yijin Chen, Xinwei Yuan, Zhuosheng Zhang, Wei Du, Yubin Zheng, Gongshen Liu

IntKB: A Verifiable Interactive Framework for Knowledge Base Completion (2020.coling-main)

Challenge:	Recent work has demonstrated the power of large language models in recalling knowledge and reasoning.
Approach:	They propose to erase shortcut neurons to mitigate the associated risks . 20% of the failures are attributed to shortcuts, they find .
Outcome:	The proposed approach reduces failures in multi-hop knowledge editing caused by shortcuts by 20% .

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Bernhard Kratzwald, Guo Kunpeng, Stefan Feuerriegel, Dennis Diefenbach

Knowledge Representation

Challenge:	Knowledge bases (KBs) present databases that store information about entities and relations among them.
Approach:	They propose a question-based interactive framework for KB completion from text . their framework generates facts that are aligned with text snippets and is immediately verifiable by humans .
Outcome:	The proposed framework achieves a hit@1 ratio of 29.7% for initial unseen relations, and gradually improves to 46.2%.

VeriFastScore: Speeding up long-form factuality evaluation (2025.findings-emnlp)

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Rishanth Rajendhran, Amir Zadeh, Matthew Sarte, Chuan Li, Mohit Iyyer

Challenge:	VeriFastScore model can be used to evaluate long-form factuality but requires multiple LLM calls and can take up to 100s to evaluate a single response.
Approach:	They propose a model that leverages synthetic data to fine-tune Llama3.1 8B for extracting and verifying all verifiable claims within a given text based on evidence from Google Search.
Outcome:	The proposed model achieves strong correlation with the original VeriScore pipeline at both the example level and system level while achieving an overall speedup of 6.6 over Veriscore.

Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)

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Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao

Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models (2026.findings-acl)

Challenge:	Existing work on pretraining models for text classification uses image encoders instead of visual prompts.
Approach:	They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning.
Outcome:	The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets.

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Sachin Kumar

SMASH: Improving SMAll Language Models’ Few-SHot Ability with Prompt-Based Distillation (2022.findings-emnlp)

Challenge:	Using a Llama-3.2-3B-Instruct backbone, we evaluate four adaptation mechanisms across four benchmarks: Gorilla APIBench, Spider 2.0, WebArena, and InterCode.
Approach:	They compare hypernetwork-based LoRA adaptation against carefully designed few-shot prompting in a controlled experiment . they find that few- shot prompting contributes +21.5% to performance and documentation contributes 0% .
Outcome:	The hypernetwork-based LoRA adaptation provides no measurable improvement over few-shot prompting alone.

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Yueqian Wang, Chang Liu, Kai Chen, Xi Wang, Dongyan Zhao

TabPrompt: Graph-based Pre-training and Prompting for Few-shot Table Understanding (2023.findings-emnlp)

Challenge:	Large-scale language models with prompts have shown remarkable performance on few-shot learning.
Approach:	They propose an approach to improve SMAll language models’ few-SHot ability by training on intermediate tasks before prompt-based fine-tuning on downstream tasks.
Outcome:	The proposed model improves on sentence-pair and sentiment classification tasks by training on intermediate tasks before fine-tuning on downstream tasks.

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Rihui Jin, Jianan Wang, Wei Tan, Yongrui Chen, Guilin Qi, Wang Hao

Synchronized Video Storytelling: Generating Video Narrations with Structured Storyline (2024.acl-long)

Challenge:	Existing methods of Table Understanding (TU) focus on the textual content within the tabular data, disregarding the topological information of the table.
Approach:	They propose a framework that uses tabs to understand tabular data without ignoring the topological information of the table.
Outcome:	The proposed framework outperforms baselines in few-shot table understanding tasks.

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Dingyi Yang, Chunru Zhan, Ziheng Wang, Biao Wang, Tiezheng Ge, Bo Zheng, Qin Jin

Are Language Models Worse than Humans at Following Prompts? It’s Complicated (2023.findings-emnlp)

Challenge:	Existing studies on dense video captioning and video story generation have made some progress, but in practical applications, we typically require synchronized narrations for ongoing visual scenes.
Approach:	They propose a task of Synchronized Video Storytelling to generate synchronized narrations for videos using a benchmark dataset with rich annotations.
Outcome:	The proposed framework can generate narrations with the guidance of the generated or predefined storyline and human evaluations validate the effectiveness.

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Albert Webson, Alyssa Loo, Qinan Yu, Ellie Pavlick

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)

Challenge:	Recent work shows that language models perform surprisingly well when given intentionally irrelevant or misleading prompts.
Approach:	They challenge an assumption that humans would perform badly when given pathological instructions by ignoring irrelevant prompts and following them faithfully when given misleading instructions.
Outcome:	The proposed model performs well when given intentionally irrelevant or misleading prompts, whereas models do not.

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Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, Maosong Sun

EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection (2025.coling-main)

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.

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Zheng Li, Sujian Li, Dawei Zhu, Qilong Ma, Weimin Xiong

“Well, Keep Thinking”: Enhancing LLM Reasoning with Adaptive Injection Decoding (2025.findings-acl)

Challenge:	Existing methods for personality detection ignore the connection between psychological knowledge “emotion regulation” and personality traits.
Approach:	They propose to use emotion regulation and emotion features to retrieve few-shot samples and provide process CoTs for inferring labels from text.
Outcome:	The proposed method outperforms SOTA by 15.05/4.29 on the two benchmark datasets.

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Hyunbin Jin, Je Won Yeom, Seunghyun Bae, Taesup Kim

Multi-Cell Compositional LSTM for NER Domain Adaptation (2020.acl-main)

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.

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Chen Jia, Yue Zhang

Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion (2022.emnlp-main)

Challenge:	Named entity recognition (NER) is a challenging but practical problem.
Approach:	They propose a multi-cell compositional LSTM structure for multi-task learning . they model each entity type using a separate cell state .
Outcome:	Empirical results show that the proposed method outperforms multi-task learning methods and achieves the best results.

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Yuling Li, Kui Yu, Xiaoling Huang, Yuhong Zhang

Multi-lingual and Multi-cultural Figurative Language Understanding (2023.findings-acl)

Challenge:	Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.
Approach:	They propose a model to learn semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.
Outcome:	The proposed model outperforms state-of-the-art methods on two public datasets.

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Anubha Kabra, Emmy Liu, Simran Khanuja, Alham Fikri Aji, Genta Winata, Samuel Cahyawijaya, Anuoluwapo Aremu, Perez Ogayo, Graham Neubig

PPT: A Minor Language News Recommendation Model via Cross-Lingual Preference Pattern Transfer (2025.acl-long)

Challenge:	Figures permeate human communication, but are understudied in NLP.
Approach:	They create a figurative language inference dataset for seven languages associated with a variety of cultures, using cultural and regional concepts for figurativ expressions.
Outcome:	The results show that the most common figurative expressions are found in Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba.

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Yiyang Zhang, Nan Chen

SymKGQA: Few-Shot Knowledge Graph Question Answering via Symbolic Program Generation and Execution (2024.acl-long)

Challenge:	Existing systems for minor language news recommendation lack interaction with content . linguistic gap can lead to inaccurate modeling of minor language content despite strong English capability .
Approach:	They propose a minor language news recommendation model by cross-lingual preference pattern transfer . their model employs the widely used two-tower architecture and large language model as the backbone of the news encoder .
Outcome:	The proposed model outperforms existing models on 15 minor languages.

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Prerna Agarwal, Nishant Kumar, Srikanta Bedathur

Analyzing the Limits of Self-Supervision in Handling Bias in Language (2022.findings-emnlp)

Challenge:	Recent advances in Large Language Models have led to low-level LFs that are limited to the knowledge of underlying LLM about the LF.
Approach:	They propose a framework that generates a symbolic LF in a few-shot setting using Large Language Models.
Outcome:	The proposed framework outperforms all other few-shot and many fully-supervised KGQA approaches.

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Lisa Bauer, Karthik Gopalakrishnan, Spandana Gella, Yang Liu, Mohit Bansal, Dilek Hakkani-Tur

Continuous Entailment Patterns for Lexical Inference in Context (2021.emnlp-main)

Challenge:	a recent study shows that natural language models can perform tasks with little to no in-context supervision . a number of tasks are performed using self-supervised pre-training .
Approach:	They define and comprehensively evaluate how well natural language taskprompting captures the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing.
Outcome:	The proposed model performs to wide varying degrees across bias dimensions . the model is largely challenged when prompted to perform these tasks .

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Martin Schmitt, Hinrich Schütze

Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2022.acl-long)

Challenge:	Pretrained language models can be used to perform lexical inference in context tasks with relatively small training data.
Approach:	They propose to combine a pretrained language model with textual patterns to improve performance in both zero-shot and few-shot settings.
Outcome:	The proposed method compares pre-trained models with textual patterns on two established benchmarks for lexical inference in context (LIiC) the results show that the proposed patterns improve performance on LIiC, setting a new state of the art.

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Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, Pontus Stenetorp

Self-play through Computational Runtimes improves Chart Reasoning (2025.findings-acl)

Challenge:	Large pretrained language models can generate text classification results that match fully supervised models.
Approach:	They propose to use a few sample training to determine which permutations are performant . they use generative language models to construct an artificial development set .
Outcome:	The proposed model outperforms fully-supervised models in eleven text classification tasks.

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Tautvydas Misiūnas, Hassan Mansoor, Jasper Uijlings, Oriana Riva, Victor Carbune

Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (2020.coling-main)

Challenge:	Vision-language models (VLMs) achieve impressive zero-shot performance on multimodal reasoning tasks.
Approach:	They propose a self-play programming interface which leverages visual reasoning models to generate code to decompose a complex visual reasoning task in sub-tasks and use it as a tool to solve decomposed tasks.
Outcome:	The proposed approach outperforms baselines on difficult chart reasoning benchmarks.

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Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei Zhang, Huajun Chen

Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency (2024.lrec-main)

Challenge:	Existing approaches to supervised relational triple extraction require huge amounts of labeled data.
Approach:	They propose a multi-prototype embedding network model to extract the composition of relational triples from unstructured text.
Outcome:	The proposed method improves the performance of the few-shot relational triple extraction problem.

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Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song, Jungyeul Park

CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021.emnlp-main)

Challenge:	generative LLMs have been known for overcorrection where results obtain higher recall measures than precision measures.
Approach:	They propose to use generative LLMs to prompt grammatical error correction using a model based on language proficiency to examine the interaction between LLM's performance and L2 language proficiency.
Outcome:	The proposed model improves on zero-shot and few-shot prompting and fine-tuning models for grammatical error correction for learners of English as a foreign language based on the different proficiency levels.

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Qinyuan Ye, Bill Yuchen Lin, Xiang Ren

StructGPT: A General Framework for Large Language Model to Reason over Structured Data (2023.emnlp-main)

Challenge:	We study whether and how cross-task generalization ability can be acquired . we use CrossFit to standardize seen/unseen task partitions and evaluation protocols .
Approach:	They propose a problem setup for studying cross-task generalization ability which standardizes seen/unseen task partitions and data access during different learning stages.
Outcome:	The proposed model can be used to build few-shot learners across diverse tasks.

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Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Xin Zhao, Ji-Rong Wen

Data-Efficient Finetuning Using Cross-Task Nearest Neighbors (2023.findings-acl)

Challenge:	Experiments conducted on three types of structured data show that StructGPT greatly improves the performance of LLMs.
Approach:	They propose an iterative Reading-then-Reasoning framework to solve question answering tasks based on structured data.
Outcome:	The proposed framework improves the reasoning ability of large language models over structured data under the few-shot and zero-shot settings.

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Hamish Ivison, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi

PPT: Pre-trained Prompt Tuning for Few-shot Learning (2022.acl-long)

Challenge:	Prior work shows training models on multitask data augmented with task descriptions transfers knowledge to new tasks.
Approach:	They propose to use unlabeled target-task data to train models on task descriptions . they use only 2% of the data from the P3 pool without labeled target task data .
Outcome:	The proposed model outperforms baseline models on 12 out of 14 datasets . it also provides better initialization than single model on target-task data .

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Yuxian Gu, Xu Han, Zhiyuan Liu, Minlie Huang

Comparative Study of Multilingual Idioms and Similes in Large Language Models (2025.coling-main)

Challenge:	Prompt tuning for pre-trained language models has shown remarkable performance . however, prompt tuning is still not fully explored .
Approach:	They propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization.
Outcome:	The proposed framework outperforms full-model tuning under full-data and few-shot learning settings.

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Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, Ali Akhgary, Samin Mahdizadeh Sani, Yadollah Yaghoobzadeh

Few-Shot Learning with Siamese Networks and Label Tuning (2022.acl-long)

Challenge:	figurative language is one of the most challenging aspects of human language for LLMs to comprehend .
Approach:	They evaluate LLMs using two multilingual datasets on simile and idiom interpretation and two new evaluation sets for Persian . they find prompt engineering methods are generally effective, but their success varies by figurative type, language, and model.
Outcome:	The proposed models perform better in simile and idiom interpretations across languages and figurative types.

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Thomas Müller, Guillermo Pérez-Torró, Marc Franco-Salvador

C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)

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.

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Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang, Shun Zhang, Jingang Wang, Zhoujun Li

CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation (2022.emnlp-main)

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.

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Abhilasha Ravichander, Matt Gardner, Ana Marasovic

A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)

Challenge:	Negation is fundamental to human communication.
Approach:	They propose a dataset which requires reasoning about implications of negated statements in paragraphs . they collect paragraphs with diverse negation cues and crowdworkers ask questions about implications .
Outcome:	The first dataset in english requires reasoning about implications of negated statements in paragraphs . it features 14,182 question-answer pairs with over 200 unique negation cues based on crowd-workers . the best performing model achieves only 42% on consistency metric, well below human performance of 81%.

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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji

DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics (2024.findings-acl)

Challenge:	Existing claims detection frameworks are portability to emerging events and low-resource training data settings.
Approach:	They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection.
Outcome:	The proposed framework outperforms baselines on the NewsClaims benchmark.

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YiQiu Guo, Yuchen Yang, Ya Zhang, Yu Wang, Yanfeng Wang

DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation (2025.findings-acl)

Challenge:	Structured data processing is a complex and complex process.
Approach:	They propose a framework that captures heterogeneity of structured data using large language models . they propose group positional encoding, hierarchical attention bias and optimal transport alignment layer .
Outcome:	The proposed framework outperforms baseline methods and few-shot GPT-4 on a medical lab report dataset.

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Eliya Habba, Ofir Arviv, Itay Itzhak, Yotam Perlitz, Elron Bandel, Leshem Choshen, Michal Shmueli-Scheuer, Gabriel Stanovsky

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking (2022.emnlp-main)

Challenge:	Recent work found that LLMs are sensitive to arbitrary prompt dimensions . this challenges traditional single-prompt evaluation practices .
Approach:	They present a large-scale dataset containing prompt perturbations of various evaluation benchmarks . they examine LLM sensitivity from an holistic perspective and assess the joint effects of perturbations along various dimensions .
Outcome:	The proposed dataset aims to democratize evaluation research and enable robust protocols . it includes more than 250M prompt perturbations and model outputs .

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Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers, Iryna Gurevych

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

Challenge:	Extensive experiments demonstrate that integrating relevance feedback directly into neural re-ranking models improves their performance.
Approach:	They propose a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant.
Outcome:	The proposed approach outperforms all other methods by 5.2% on large-scale information retrieval datasets.

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Anar Yeginbergen, Maite Oronoz, Rodrigo Agerri

Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (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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Bin Li, Yunlong Fan, Yikemaiti Sataer, Chuanqi Shi, Miao Gao, Zhiqiang Gao

Tab-CoT: Zero-shot Tabular Chain of Thought (2023.findings-acl)

Challenge:	Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele .
Approach:	They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data.
Outcome:	The proposed framework achieves promising results when few-shot training samples are available.

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Jin Ziqi, Wei Lu

PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models (2023.findings-emnlp)

Challenge:	Recent efforts to encourage more structured reasoning procedures to be captured have shown that chain-of-though (CoT) prompting methods can be effective in NLP tasks.
Approach:	They propose a tabular-format CoT prompting method that allows the complex reasoning process to be explicitly modeled in a highly structured manner.
Outcome:	The proposed method shows impressive performance improvements on a range of reasoning tasks.

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Xingwei He, A-Long Jin, Jun Ma, Yuan Yuan, Siu Yiu

FarExStance: Explainable Stance Detection for Farsi (2025.coling-main)

Challenge:	Existing methods for Factual Error Correction (FEC) use mask-then-correct paradigms . however, the lack of datasets containing false claims has impeded progress .
Approach:	They propose a method that enhances few-shot FEC with a pivot task approach using large language models.
Outcome:	The proposed method outperforms its few-shot counterpart by 7.9 points in SARI . it improves widely-adopted SARI metrics by 11.3 compared to the best-performing methods .

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Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh, Sauleh Eetemadi, Mohammad Taher Pilehvar, Jennifer Foster

Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models (2025.findings-emnlp)

Challenge:	FarExStance is a new dataset for explainable stance detection in Farsi . it contains extractive explanations as evidence for stance labels and claims .
Approach:	They propose a dataset for explainable stance detection in Farsi with extractive explanations as evidence.
Outcome:	The proposed model is the most accurate on stance detection, while the best explanation is from few-shot Claude-3.5-Sonnet.

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Cheng Yan, Feng Zhao, Ruilin Zhao, Hong Zhang

Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs (2024.findings-emnlp)

Challenge:	Current knowledge graph reasoning methods rely on extensive structural information to perform in these few-shot scenarios.
Approach:	They propose a task-aware language model that activates the language model’s in-context learning ability for structured KG tasks.
Outcome:	The proposed method achieves state-of-the-art in few-shot scenarios while reducing the inference time required by previous methods.

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John Mendonça, Isabel Trancoso, Alon Lavie

OpineSum: Entailment-based self-training for abstractive opinion summarization (2023.findings-acl)

Challenge:	Current evaluation practices of open domain dialogue systems are still highly dependent on human evaluation.
Approach:	They propose to use an annotated dataset to evaluate chatbots using large language models.
Outcome:	The proposed model improves over few-shot inferences on a GPT-3.5 generated dialogue dataset.

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Annie Louis, Joshua Maynez

Summarization

Challenge:	Abstractive summarization is promising for fluently comparing opinions from a set of reviews about a place or product.
Approach:	They propose a novel method that automatically leverages common opinions across reviews to create powerful abstractive models.
Outcome:	The proposed method outperforms strong peer systems in both settings.

Can Large Language Models Understand Argument Schemes? (2025.findings-acl)

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Elfia Bezou-Vrakatseli, Oana Cocarascu, Sanjay Modgil

Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions? (2025.findings-acl)

Challenge:	Argument schemes are stereotypical forms of reasoning that occur in everyday arguments.
Approach:	They propose to use large language models (LLMs) to classify argument schemes based on Walton’s taxonomy to employ formal definitions and LLM-generated descriptions to enhance task instructions.
Outcome:	The proposed models perform well on annotated and automatically generated arguments, and provide insights for advancing reasoning capabilities in computational argumentation.

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Xianren Zhang, Xianfeng Tang, Hui Liu, Zongyu Wu, Qi He, Dongwon Lee, Suhang Wang

Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning (2021.emnlp-main)

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.

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Prasetya Utama, Nafise Sadat Moosavi, Victor Sanh, Iryna Gurevych

Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models (2024.findings-emnlp)

Challenge:	Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream task instances as a language modeling problem.
Approach:	They propose to reformulate downstream tasks as a language modeling problem and add a regularization that preserves pretraining weights to the model to mitigate the destructive tendency of few-shot finetuning.
Outcome:	The proposed model performs better on low data regimes than the standard model on few-shot finetuning.

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Kang He, Yinghan Long, Kaushik Roy

Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)

Challenge:	Prompt-based learning is susceptible to intrinsic bias present in pre-trained language models (LMs), leading to sub-optimal performance in prompt-based zero/few-shot settings.
Approach:	They propose a null-input prompting method to calibrate intrinsic bias encoded in pre-trained language models (LMs) they leverage a diverse set of auto-selected null meaning inputs generated from GPT-4 to probe intrinsic bias.
Outcome:	The proposed method significantly improves zero/few-shot learning performance of LMs for both in-context learning and prompt-based fine-tuning (on average 9% and 2%, respectively).

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Laura Perez-Beltrachini, Mirella Lapata

CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision (2022.emnlp-main)

Challenge:	Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs.
Approach:	They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language.
Outcome:	The proposed task can be applied to several other languages and covers twelve languages and directions.

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Yuning Mao, Ming Zhong, Jiawei Han

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers . previous attempts failed to scale up due to heavy human annotation and domain expertise .
Approach:	They propose a method to automatically extract TLDR summaries from scientific papers . they propose 'citeSum' with no human annotation, which is 30 times larger than SciTLDR .
Outcome:	The proposed approach outperforms most fully-supervised methods on SciTLDR without fine-tuning and achieves state-of-the-art results with only 128 examples.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation (2024.acl-long)

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

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Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum

Let’s Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models’ Understanding of Sports (2025.emnlp-main)

Challenge:	Low-resource tasks such as semi-supervised sequence generation require expert knowledge and cost.
Approach:	They propose a method for semi-supervised sequence generation where few examples are too scarce to fine tune a model.
Outcome:	The proposed method can generalize better than its teacher to unseen examples on semi-supervised sequence generation tasks.

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Punit Kumar Singh, Nishant Kumar, Akash Ghosh, Kunal Pasad, Khushi Soni, Manisha Jaishwal, Sriparna Saha, Syukron Abu Ishaq Alfarozi, Asres Temam Abagissa, Kitsuchart Pasupa, Haiqin Yang, Jose G Moreno

Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)

Challenge:	Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions.
Approach:	They propose to use multiple-choice questions (MCQs) to assess LMs' understanding of traditional sports across 60 countries and 6 continents.
Outcome:	The new benchmark will be publicly available, fostering research in culturally aware AI systems.

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Zhiyin Yu, Bo Zhang, Qibin Hou, Zhonghai Wu, Xiao Luo, Lei Bai

Social Bias Evaluation for Large Language Models Requires Prompt Variations (2025.findings-emnlp)

Challenge:	Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking.
Approach:	They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data.
Outcome:	The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data.

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Rem Hida, Masahiro Kaneko, Naoaki Okazaki

DSPM-NLG: A Dual Supervised Pre-trained Model for Few-shot Natural Language Generation in Task-oriented Dialogue System (2023.findings-acl)

Challenge:	Recent studies have tried to evaluate and mitigate social biases accurately using limited prompts.
Approach:	They investigate the sensitivity of Large Language Models when changing prompt variations . they found that LLM rankings fluctuate across prompts for both task performance and social bias .
Outcome:	The results show that LLM rankings fluctuate when changing prompt variations .

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

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)

Challenge:	Existing models for few-shot natural language generation are based on a dual correlation between NLG and SLU from the perspective of probability.
Approach:	They propose a dual supervised pre-trained model to regularize the pre-training process . they use a probabilistic approach to learn the dual correlation between NLG and SLU .
Outcome:	The proposed model outperforms the previous state-of-the-art models on a few-shot dataset.

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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar

DiZiNER: Disagreement-guided Instruction Refinement via Simulating Pilot Annotation for Zero-shot Named Entity Recognition (2026.acl-long)

Challenge:	Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning.
Approach:	They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database.
Outcome:	The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods.

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Siun Kim, Hyung-Jin Yoon

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)

Challenge:	Large language models have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER) but their outputs still show persistent and systematic errors.
Approach:	They propose a framework that simulates the pilot annotation process and employs LLMs as both annotators and supervisors to refine model disagreements.
Outcome:	Using a pilot annotation process, the proposed framework outperforms its supervisor model on 18 benchmarks.

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Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong

Representational Isomorphism and Alignment of Multilingual Large Language Models (2024.findings-emnlp)

Challenge:	Modern large language models often "hallucinate" plausible but factually incorrect information, which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical.
Approach:	They develop a human evaluation procedure to measure correctness and hallucination and use it to benchmark both closed and open-source LLMs.
Outcome:	The proposed method outperforms both competing search engine-augmented prompting methods and commercial systems on search-augmented QA.

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Di Wu, Yibin Lei, Andrew Yates, Christof Monz

LLMs to Replace Crowdsourcing For Parallel Data Creation? The Case of Text Detoxification (2024.findings-emnlp)

Challenge:	Existing isomorphism of sentence representations can facilitate representational alignments in zero-shot and few-shot settings.
Approach:	They propose to apply a contrastive objective to LLMs with a small number of translation pairs to improve models' performance on Semantic Textual Similarity tasks.
Outcome:	The proposed representation-level approach significantly improves on Semantic Textual Similarity (STS) tasks across languages even without a monolingual objective.

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Daniil Moskovskiy, Sergey Pletenev, Alexander Panchenko

Dual-Stage Multi-Task Syntax-Oriented Pre-Training for Syntactically Controlled Paraphrase Generation (2024.findings-acl)

Challenge:	Manual annotation methods, such as crowdsourcing, are costly and require intricate task design skills.
Approach:	They propose to use open source LLMs to annotate parallel data for text detoxification . they generate a pseudo-parallel detoxification dataset using activation patching .
Outcome:	The proposed model performs comparable to the original dataset in automatic detoxification evaluation metrics and superior quality in manual evaluation and side-by-side comparisons.

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Hongxu Liu, Xiaojie Wang, Jiashen Sun, Ke Zeng, Wan Guanglu

Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)

Challenge:	Syntactically controlled paraphrase generation (SCPG) aims to generate sentences with syntactic structures resembling given exemplars.
Approach:	They propose a dual-stage multi-task pre-training scheme that uses a series of structure-oriented and syntax-oriented tasks to generate sentences with syntactic structures resembling given exemplars.
Outcome:	The proposed method outperforms existing methods on all possible variants of SCPG tasks and significantly outperformed the popular T5 model.

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Yi Su, Yunpeng Tai, Yixin Ji, Juntao Li, Yan Bowen, Min Zhang

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

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.

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

Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)

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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Xuandong Zhao, Siqi Ouyang, Zhiguo Yu, Ming Wu, Lei Li

Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning (2023.findings-emnlp)

Challenge:	Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts.
Approach:	They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment .
Outcome:	The proposed method outperforms previous methods on diverse tasks.

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Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen

LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)

Challenge:	Prompt-based learning has been an effective paradigm for large pretrained language models (LLMs), enabling few-shot or even zero-shot learning.
Approach:	They propose a black-box prompt search method that clusters and prunes the search space to focus exclusively on influential prompt tokens.
Outcome:	The proposed method achieves state-of-the-art performance across tasks and LLMs while significantly reducing search costs.

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Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou, Lei Zou

Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing (2025.findings-emnlp)

Challenge:	Existing supervised learning methods in natural language processing require large amounts of data.
Approach:	They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently.
Outcome:	The proposed model outperforms existing models with few-shot performance in two NLP tasks.

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Jongwoo Kim, SeongYeub Chu, Bryan Wong, Mun Yong Yi

Language Models for Text Classification: Is In-Context Learning Enough? (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have strong reasoning and generalization abilities, but they struggle to reflect the histories of example learners within a single prompt during in-context learning.
Approach:	They propose a LLM-based option weighted knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights.
Outcome:	The proposed framework outperforms existing LLM-based KT models in warm-start and few-shot settings.

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Aleksandra Edwards, Jose Camacho-Collados

XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations (2023.acl-long)

Challenge:	Existing research on text classification models with prompts is limited in scale and lacks understanding of how these methods compare to more established methods.
Approach:	They compare the performance of large and smaller language models with prompts to achieve state-of-the-art performance in many NLP tasks.
Outcome:	The proposed models outperform the more standard approaches in binary, multiclass, and multilabel tasks in a large scale evaluation of 16 text classification datasets.

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Yusen Zhang, Jun Wang, Zhiguo Wang, Rui Zhang

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)

Challenge:	Existing models for cross-lingual semantic parsing are not able to perform tasks on a wide range of datasets.
Approach:	They propose a benchmark for cross-lingual semantic parsing that uses 22 natural languages and 8 meaning representations to translate queries into MRs.
Outcome:	The proposed benchmarks cover 22 natural languages and 8 meaning representations on 164 domains and 5 tasks covering a wide range of multilingual language models.

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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

API-Assisted Code Generation for Question Answering on Varied Table Structures (2023.emnlp-main)

Challenge:	Existing methods for data-to-text generation focus on specific types of structured data.
Approach:	They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations.
Outcome:	The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data.

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Yihan Cao, Shuyi Chen, Ryan Liu, Zhiruo Wang, Daniel Fried

A Comprehensive Evaluation of Tool-Assisted Generation Strategies (2023.findings-emnlp)

Challenge:	Existing approaches to table question answering have been limited to specific table structures.
Approach:	They propose a unified TableQA framework that uses Python as a querying language and few-shot prompting to translate NL questions into Python programs.
Outcome:	The proposed framework provides a unified representation for structured tables as multi-index Pandas data frames and uses Python as a powerful querying language to translate NL questions into Python programs.

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Alon Jacovi, Avi Caciularu, Jonathan Herzig, Roee Aharoni, Bernd Bohnet, Mor Geva

Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups (2024.emnlp-main)

Challenge:	Various few-shot tool-usage strategies have been proposed to overcome LMs' shortcomings.
Approach:	They propose to augment language models with tools to overcome their shortcomings . they find strong no-tool baselines are competitive to tool-assisted strategies .
Outcome:	The proposed strategies outperform those that refine incorrect outputs with tools in knowledge-retrieval tasks, the study finds . the findings suggest few-shot tool integration is still an open challenge .

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Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel

BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions (2023.findings-emnlp)

Challenge:	Large language models (LLMs) are popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings.
Approach:	They investigate the use of large language models in CWI, LCP, and MWE settings by evaluating their use in zero-shot, few-shot and fine-tuning settings.
Outcome:	The proposed models struggle in certain conditions or achieve comparable results against existing methods.

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Arth Bohra, Govert Verkes, Artem Harutyunyan, Pascal Weinberger, Giovanni Campagna

Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions (2025.findings-emnlp)

Challenge:	Existing approaches to text classification require large annotated corpora to train or long context to fit many examples.
Approach:	They propose a method to few-shot text classification using an LLM.
Outcome:	The proposed approach yields high accuracy classifiers within 79% of the performance of models trained with larger datasets while using only 1% of their training sets.

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Meghdut Sengupta, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Debanjan Ghosh, Henning Wachsmuth

Challenge:	Metaphorical language is prevalent in everyday communication, often used unconsciously, as in "rising crime" incorporating conceptual metaphors improves performance on downstream tasks that implicitly require correct metaphor interpretation, such as NLI.
Approach:	They extend metaphorical texts in an existing NLI dataset by source and target domains and conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI.
Outcome:	The proposed method improves performance on natural language inference tasks with metaphoric input.

IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery (2023.findings-emnlp)

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Bhavuk Singhal, Ashim Gupta, V P Shivasankaran, Amrith Krishna

DeCoVec: Building Decoding Space based Task Vector for Large Language Models via In-Context Learning (2026.findings-acl)

Challenge:	Intent-related tasks are typically modeled as separate tasks, but a unified approach is proposed . INTENDD uses an entirely unsupervised contrastive learning strategy for representation learning .
Approach:	They propose a unified approach to identifying intents from dialogue utterances . they propose an unsupervised contrastive learning strategy for representation learning .
Outcome:	The proposed approach outperforms baselines on three intent-related tasks on multiple datasets.

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Feiyang Li, Yile Wang

Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives (2024.lrec-main)

Challenge:	Existing approaches to steering large language models require fine-tuning or manipulation of internal states, limiting their flexibility and scalability.
Approach:	They propose a framework that constructs task vectors directly in the decoding space by leveraging in-context learning.
Outcome:	The proposed framework outperforms standard few-shot baselines on TruthfulQA, Math-500, and AQUA-RAT with gains up to +5.50 accuracy.

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Qiushi Sun, Chengcheng Han, Nuo Chen, Renyu Zhu, Jingyang Gong, Xiang Li, Ming Gao

KEEP CHATTING! An Attractive Dataset for Continuous Conversation Agents (2024.findings-acl)

Challenge:	Large language models (LLMs) have shown increasing power on NLP tasks. however, tuning these models for downstream tasks usually requires exorbitant costs.
Approach:	They propose a black-box tuning technique that optimizes task-specific prompts without accessing gradients and hidden representations.
Outcome:	The proposed method improves performance under few-shot learning scenarios.

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Yihe Wang, Jin Liu, Yao Wan, Yitong Li, Zifeng Liu, Weipeng Chen

Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue (2023.emnlp-main)

Challenge:	Existing works about persona dialogue such as PersonaChat have greatly facilitated the chatbot with configurable and persistent personalities.
Approach:	They propose to collect a dataset called ContinuousChat and rewrite it in style-specific ways to increase users' willingness to continue chatting.
Outcome:	The proposed model increases users' willingness to continue talking to the chatbot by increasing their personas to detailed-personas through experiences, daily life, future plans, or interesting stories.

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Yizhe Yang, Heyan Huang, Yuhang Liu, Yang Gao

Natural Language Interfaces

Challenge:	Knowledge-grounded dialogue systems can generate informative responses based on dialogue history and external knowledge source.
Approach:	They conduct a thorough experiment to determine the optimal knowledge form, mutual effects between knowl- edge and model selection, and the few-shot performance of knowledge.
Outcome:	The proposed method combines knowledge-grounded dialogue with human-generated dialogues to generate informative and meaningful responses.

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations (2025.acl-long)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Gaurav Verma, Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Tucker Balch, Manuela Veloso

Classifying Unreliable Narrators with Large Language Models (2025.acl-long)

Challenge:	State-of-the-art multimodal web agents can perform many web tasks by processing user instructions and interacting with graphical user interfaces (GUIs).
Approach:	They propose to build multimodal web agents for few-shot adaptability using human demonstrations to improve their generalization and adaptability.
Outcome:	The proposed framework enables both proprietary and open-weights multimodal web agents to adapt to new websites and domains using few human demonstrations.

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Anneliese Brei, Katharine Henry, Abhisheik Sharma, Shashank Srivastava, Snigdha Chaturvedi

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)

Challenge:	a recent study identifies unreliable narrators, i.e. those who unintentionally misrepresent information . authors propose using computational methods to identify unredependable narrators . adbrei: readers implicitly question the reliability of the nrator .
Approach:	They propose using computational methods to identify unreliable narrators . they use literary theory to define different types of unredependable narrators .
Outcome:	The proposed method can identify unreliable narrators on real-world text data.

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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu

Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data (2023.findings-emnlp)

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples.
Approach:	They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE.
Outcome:	The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

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Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, Arpit Patil, Oana Cocarascu, Elena Simperl

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)

Challenge:	Recent benchmarks have assessed language models' numerical abilities . limitations include tokenization and representation of numbers in text, hallucination, and a lack of numerical commonsense knowledge.
Approach:	They propose a hierarchical taxonomy for numerical reasoning skills that includes representation, number sense, manipulation, and complex reasoning.
Outcome:	The proposed model outperforms other models on the tabular Natural Language Inference task.

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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li

Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction (2025.findings-acl)

Challenge:	Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios.
Approach:	They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings.
Outcome:	The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains.

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Guangyue Peng, Wei Li, Wen Luo, Houfeng Wang

Representation Learning

Challenge:	a novel method for encoding fine-grained error patterns improves performance on GEC.
Approach:	They propose a method for encoding grammatical errors from LLMs' internal states using a GER method.
Outcome:	The proposed method significantly boosts performance in ICL settings on multilingual GEC datasets.

Can Input Attributions Explain Inductive Reasoning in In-Context Learning? (2025.findings-acl)

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Mengyu Ye, Tatsuki Kuribayashi, Goro Kobayashi, Jun Suzuki

PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification (2024.lrec-main)

Challenge:	interpreting the internal process of neural models has long been a challenge . despite rapid progress, there are still questions bridging the IA and MI eras .
Approach:	They propose to use input attribution methods to interpret in-context learning . they find that a certain simple IA method works best in large models .
Outcome:	The proposed method is the best for interpreting LLM-based ICL, but the larger the model, the harder it is to interpret it.

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Xurui Li, Kaisong Song, Tianqianjing Lin, Yangyang Kang, Fubang Zhao, Changlong Sun, Xiaozhong Liu

Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement (2026.findings-acl)

Challenge:	Existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance.
Approach:	They propose a meta-learning framework with progressive data augmentation for few-shot text classification using prompt-based data augmented by attention-based methods.
Outcome:	The proposed framework outperforms state-of-the-art models and shows better robustness on four public few-shot text classification datasets.

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Midan Shim, Seokju Hwang, KaeHyun Um, Kyong-Ho Lee

AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic (2025.findings-acl)

Challenge:	Existing KGQA benchmarks and methods are biased toward positive and calculation constraints. Negative constraints are neglected, although they frequently appear in real-world questions.
Approach:	They propose a task where each question contains at least one negative constraint and a corresponding dataset, NestKGQA.
Outcome:	The proposed framework outperforms baselines on both KGQA and NEST-KGQA benchmarks under few-shot settings.

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Nathaniel Romney Robinson, Shahd Abdelmoneim, Kelly Marchisio, Sebastian Ruder

Challenge:	Dialectal Arabic (DA) varieties are under-served by language technologies, particularly large language models (LLMs).
Approach:	They propose a framework that comprehensively assesses LLMs’ DA modeling capabilities across four dimensions: fidelity, understanding, quality, and diglossia.
Outcome:	The proposed framework assesses LLMs’ DA modeling capabilities across four dimensions: fidelity, understanding, quality, and diglossia.

Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts (2024.emnlp-main)

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Arianna Muti, Federico Ruggeri, Khalid Khatib, Alberto Barrón-Cedeño, Tommaso Caselli

ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts (2025.emnlp-main)

Challenge:	a new study aims to understand the implicit reasoning used to convey misogynistic comments in Italian and English.
Approach:	They propose misogyny detection as an Argumentative Reasoning task and use argumentation theory to build large language models to understand the implicit reasoning used to convey misogany in Italian and English.
Outcome:	The proposed task is an argumentative reasoning task in Italian and English.

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Ruiran Su, Jiasheng Si, Zhijiang Guo, Janet B. Pierrehumbert

Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding (2024.lrec-main)

Challenge:	Scientific fact-checking has largely focused on textual and tabular sources, neglecting scientific charts.
Approach:	They propose a benchmark for scientific fact-checking grounded in scientific charts . climateViz comprises 49,862 claims paired with 2,896 visualizations . results show current models struggle to perform fact- checking when statistical reasoning is required .
Outcome:	The climateviz benchmark is the first large-scale benchmark for scientific fact-checking . it includes 49,862 claims paired with 2,896 visualizations labeled as support, refute, or not enough .

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Qingyan Zhao, Ruifang He, Jinpeng Zhang, Chang Liu, Bo Wang

Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection (2025.findings-emnlp)

Challenge:	Prompt-based fine-tuning (PF) models have shown improved performance on few-shot natural language understanding benchmarks.
Approach:	They propose a framework to alleviate anisotropy in the embedding space by aligning with pre-trained language models' training objective.
Outcome:	The proposed method outperforms mainstream methods on many NLU benchmarks.

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Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro

Multilingual Definition Modeling (2025.findings-acl)

Challenge:	Specifically, we tried native sample mixing, multi-task learning, and prompting and instruction finetuning very large multilingual language models (VMLMs).
Approach:	They used native sample mixing, multi-task learning and prompting and instruction finetuning to improve code-mixed humour and sarcasm detection.
Outcome:	The proposed methods improve humour and sarcasm detection by adding native samples to training sets and multitask learning and prompting and instruction finetuning VMLMs.

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Edison Marrese-Taylor, Erica K. Shimomoto, A. Solano, Enrique Reid

Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation (2025.acl-long)

Challenge:	Existing definition modeling tasks are mainly encoder-decoder-based, with no explicit definitions.
Approach:	They propose a multilingual study on definition modeling using monolingual dictionary data for four new languages.
Outcome:	The proposed task is based on monolingual dictionary data for four new languages . results show that multilingual models can perform on-pair with English but cannot leverage potential cross-lingual synergies .

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Soumitra Ghosh, Gopendra Vikram Singh, Shambhavi Shambhavi, Sabarna Choudhury, Asif Ekbal

TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition (2024.lrec-main)

Challenge:	Self-harm detection on social media is critical for early intervention and mental health support, yet remains challenging due to the subtle, context-dependent nature of such expressions.
Approach:	They propose a framework to distinguish intent through nuanced language–emoji interplay.
Outcome:	The proposed framework improves self-harm detection and explanation tasks on three state-of-the-art LLMs.

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Yuanyuan Xu, Linhai Zhang, Deyu Zhou

Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)

Challenge:	Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner.
Approach:	They propose a span-based method with a controllable attention soft prompt for few-shot nested named entity recognition (TECA) the span part identification provides possible entity mentions without an extra filtering module.
Outcome:	The proposed method outperforms baseline models on four benchmark datasets and outperformed competing models on F1-score by 5.62% on ACE04, 5.11% on ace05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting.

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Qi Zhang, Fangping Lan, Cornelia Caragea, Longin Jan Latecki, Eduard Dragut

Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents (2024.lrec-main)

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.

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Hao Wang, Tang Li, Chenhui Chu, Rui Wang, Pinpin Zhu

Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)

Challenge:	Existing document AI approaches fail to consider key-value relations in visually-rich documents . a few-shot approach is proposed to extract key- value relation triplets in VRDs .
Approach:	They propose a few-shot relational learning approach targeting the extraction of key-value relation triplets in Visually-Rich Documents.
Outcome:	The proposed method outperforms existing methods in visually-rich documents.

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Fahmida Alam, Md Asiful Islam, Robert Vacareanu, Mihai Surdeanu

InsAT: Instance-aware Semantic Alignment and Transfer from Human–Object Keypoints for Zero-to-Few-shot Action Understanding (2026.acl-long)

Challenge:	Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required.
Approach:	They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset.
Outcome:	The proposed methods perform poorly on the few-shot relation extraction task.

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Kazuki Tsutsukawa

Activation Reward Models for Few-Shot Model Alignment (2026.findings-acl)

Challenge:	Existing zero-shot (ZS) approaches emphasize human motion while underutilizing contextual information, particularly human–object interactions.
Approach:	They propose a framework for ZS recognition and zero-to-few-shot adaptation that leverages instance-level language descriptions.
Outcome:	The proposed framework outperforms keypoint-based ZS methods while remaining data-efficient and robust.

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Tianning Chai, Chancharik Mitra, Brandon Huang, Gautam Rajendrakumar Gare, Zhiqiu Lin, Assaf Arbelle, Leonid Karlinsky, Rogerio Feris, Trevor Darrell, Deva Ramanan, Roei Herzig

False Friends or Cognates? A Cross-lingual Semantic Ambiguity Evaluation for Galician, Portuguese and Spanish (2026.acl-long)

Challenge:	A common approach is to use reward models that enable reinforcement-learning post-training.
Approach:	They propose a method that steers LLM activations to align with few-shot preference data without finetuning.
Outcome:	The proposed method surpasses zero-shot, few-shot and voting-based benchmarks on reward hacking and noise signals.

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Marta Vázquez Abuín, Jose Camacho-Collados, Marcos Garcia

Many-Shot Scaling of In-Context Learning with Self-Generated Demonstrations (2026.findings-acl)

Challenge:	Closely related languages exhibit a high degree of lexical and orthographic similarity, which can facilitate cross-lingual understanding but also give rise to systematic semantic ambiguity.
Approach:	They introduce six cross-lingual datasets that are manually or semi-automatically generated and are able to identify and process false friends among these languages.
Outcome:	The proposed models can identify and process false friends among Galician, Portuguese, and Spanish.

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Zhengyao Gu, Henry Peng Zou, Yankai Chen, Aiwei Liu, Weizhi Zhang, Philip S. Yu