Papers with zero-shot setting
Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling (2024.findings-emnlp)
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| Challenge: | Existing studies in cross-lingual semantic role labeling (SRL) lack a comprehensive analysis of their network selection. |
| Approach: | They compare the transferability of graph neural network-based models with universal dependency trees to English and 23 target languages. |
| Outcome: | The proposed models perform better in resource-poor languages than in resource rich ones. |
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world (2023.acl-tutorials)
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| Challenge: | Responsible AI issues such as fairness, bias and toxicity will be discussed in this tutorial . |
| Approach: | This tutorial will describe various aspects of scaling up language technologies to many of the world’s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). |
| Outcome: | This tutorial will cover various aspects of scaling up language technologies to many of the world's languages by describing the latest research in multilingual models. |
Modularized Zero-shot VQA with Pre-trained Models (2023.findings-acl)
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| Challenge: | Recent work on zero-shot visual question answering does not explicitly consider multi-step reasoning chains, making them less interpretable compared with a decomposition-based approach. |
| Approach: | They propose a modularized zero-shot network that explicitly decomposes questions into sub reasoning steps and is highly interpretable. |
| Outcome: | The proposed model decomposes questions into sub reasoning steps and is highly interpretable. |
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models? (2024.findings-eacl)
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| Challenge: | Existing studies show that only the textual component of hateful memes enables the multimodal classifier to generalize across domains while the image component proves highly sensitive to a specific training dataset. |
| Approach: | They propose to use only the textual component of hateful memes to generalize across different domains while the image component is highly sensitive to a specific training dataset. |
| Outcome: | The proposed model performs similarly to hate-meme classifiers in a zero-shot setting, while the introduction of meme’s image captions worsens performance by an average F1 of 0.02. |
Turkish Universal Conceptual Cognitive Annotation (2022.lrec-1)
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| Challenge: | UCCA-annotated datasets have been released in English, French, and German . a semi-automatic annotation approach is used to annotate the datasets . |
| Approach: | They propose to use an external semantic parser to annotate Turkish sentences . they use the same parsers for evaluation purposes and conducted experiments . |
| Outcome: | The proposed dataset is the first UCCA-annotated Turkish dataset . the results show that the parser can improve on the initial annotations . |
Coreference Resolution through a seq2seq Transition-Based System (2023.tacl-1)
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| Challenge: | Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference. |
| Approach: | They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly. |
| Outcome: | The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese. |
Multilingual Autoregressive Entity Linking (2022.tacl-1)
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Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni
| Challenge: | mGENRE is a sequence-to-sequence system for multilingual entity linking . mGenRE is used to solve language-specific mentions to a multilingual Knowledge Base . |
| Approach: | They propose a sequence-to-sequence system for multilingual entity linking . they match language-specific mentions against a multilingual Knowledge Base (KB) mGENRE is a sequential system that predicts the name of the target entity token-by-token . |
| Outcome: | The proposed system improves on three popular MEL benchmarks and shows improvements in accuracy. |
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA (2024.naacl-long)
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| Challenge: | Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. |
| Approach: | They propose a framework to explicitly utilize the massive knowledge encoded in LLM parameters and their strong instruction understanding abilities. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on three widely-used ODQA datasets and achieves comparable performance with customized fine-tuned models on full training data. |
Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)
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| Challenge: | Recent work improves on the success of monolingual pretrained language models by adding cross-lingual tasks that always involve English. |
| Approach: | They propose a method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of pretrained language models. |
| Outcome: | The proposed model outperforms XLM-R_Base on translation-train tasks while using less parallel data and fewer parameters. |
BanglaSocialBench: A Benchmark for Evaluating Sociopragmatic and Cultural Alignment of LLMs in Bangladeshi Social Interaction (2026.acl-srw)
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| Challenge: | Large Language Models have demonstrated strong multilingual fluency, but not socially appropriate language use is guaranteed. |
| Approach: | They propose a benchmark to evaluate sociopragmatic competence in Bangla through context-dependent language use rather than factual recall. |
| Outcome: | The proposed benchmark evaluates sociopragmatic competence in Bangla through context-dependent language use rather than factual recall. |
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
| Approach: | They propose a meta-learning framework to learn shareable structures from typologically diverse languages based on meta- learning and language clustering. |
| Outcome: | The proposed framework is able to learn shareable structures from typologically diverse languages with limited annotated data. |
BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra (2024.findings-acl)
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| 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. |
Generating Questions from Wikidata Triples (2022.lrec-1)
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| Challenge: | Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple. |
| Approach: | They revisit KBQG using pre training, a new (triple, question) dataset and taking question type into account and provide a more extended KBqg dataset. |
| Outcome: | The proposed approach outperforms existing methods in a standard and in 'zero-shot' setting. |
ColorFoil: Investigating Color Blindness in Large Vision and Language Models (2025.naacl-srw)
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| Challenge: | Several studies indicate a lack of robustness of the models when dealing with complex linguistics and visual attributes. |
| Approach: | They propose a new V&L benchmark by creating color-related foils to assess the models’ perception ability to detect colors like red, white, green, etc. |
| Outcome: | The proposed benchmark evaluates seven state-of-the-art V&L models including CLIP, ViLT, GroupViT, and BridgeTower in a zero-shot setting and demonstrates that they have better color perception capabilities than CLIP and its variants and GroupVit. |
Cross-Domain Generalization of Neural Constituency Parsers (P19-1)
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| Challenge: | Neural parsers perform well on in-domain benchmarks, but their performance degrades in well-understood ways. |
| Approach: | They analyze generalization on English and Chinese corpora to see if they can generalize to other domains. |
| Outcome: | The proposed neural parsers perform better on in-domain benchmarks than on out-of-domain corpora. |
Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
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| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
| Outcome: | The proposed approach achieves 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system which uses multi-lingual training and back-translation. |
Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages (2023.acl-short)
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| Challenge: | Existing studies have shown that the pre-training in English does not transfer well to other languages in a zero-shot setting. |
| Approach: | They propose a simple yet efficient approach to adapt VLP to unseen languages using MPLM. |
| Outcome: | The proposed approach outperforms state-of-the-art models without large parallel corpora across three tasks. |
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2024.findings-acl)
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Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, Mark Gerstein
| Challenge: | Large language models face unique challenges such as domain-specific terminologies and reasoning over specialized knowledge. |
| Approach: | They propose a multi-disciplinary collaboration framework that leverages LLM-based agents in a role-playing setting. |
| Outcome: | The proposed framework excels at mining and harnessing medical expertise within LLMs, as well as extending its reasoning abilities. |
Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs (2022.acl-srw)
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| Challenge: | Existing methods for analyzing knowledge graphs focus on concept relations and clinical processes. |
| Approach: | They propose to extract clinical knowledge graphs from a wiki and consumer health resource texts by using a clinical reasoning ontology. |
| Outcome: | The proposed methods evaluate the correctness of extracted triples in the zero-shot setting. |
Hybrid Graphs for Table-and-Text based Question Answering using LLMs (2025.naacl-long)
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| Challenge: | Current methods for QA rely on fine-tuning and high-quality data, which is difficult to obtain. |
| Approach: | They propose a Hybrid Graph-based approach for Table-Text QA that leverages Large Language Models without fine-tuning. |
| Outcome: | The proposed approach improves Exact Match scores by 10% on Hybrid-QA and 5.4% on OTT-QA. |
Large Language Models can Share Images, Too! (2024.findings-acl)
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| Challenge: | Using a zero-shot prompting, large language models can be used to share images in a multi-tasking environment. |
| Approach: | They introduce a dataset that includes enriched annotations and a framework to evaluate LLMs. |
| Outcome: | The proposed framework unlocks image-sharing capability of LLMs in zero-shot prompting, with ChatGPT achieving the best performance. |
Don’t Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings (2020.emnlp-main)
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| Challenge: | Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning. |
| Approach: | They show that English dev accuracy makes it difficult to obtain reproducible results . they recommend providing oracle scores alongside zero-shot results if possible . |
| Outcome: | mBERT and XLM have shown strong performance on cross-lingual recognition, text classification, dependency parsing, and other tasks. |
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2021.naacl-main)
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Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel
| Challenge: | Current natural language processing pipelines often use transfer learning, where a model is pre-trained on a data-rich task before being fine-tuned on . this significantly limits their use given that roughly 80% of the world population does not speak English. |
| Approach: | They introduce a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. |
| Outcome: | The proposed model achieves state-of-the-art on multilingual benchmarks and a simple technique to prevent accidental translation in the zero-shot setting. |
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? (2022.lrec-1)
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| Challenge: | Keyword extraction is the task of retrieving words that are essential to the content of a document. |
| Approach: | They propose to use pretrained multilingual language models for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data. |
| Outcome: | The proposed models outperform state-of-the-art unsupervised methods on low-resource languages with limited or no training data. |
Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection (2023.findings-emnlp)
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| Challenge: | Intent discovery remains a crucial task in natural language processing . identifying novel, unseen intents remains one of the biggest challenges in this field . |
| Approach: | They propose a multi-language approach to intent discovery using Adapters and a Transformer architecture. |
| Outcome: | The proposed pipeline outperforms baselines in two zero-shot settings for intent classification and unseen intent discovery. |
COSY: COunterfactual SYntax for Cross-Lingual Understanding (2021.acl-long)
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| Challenge: | Pre-trained multilingual language models suffer from a large performance gap between source and target languages . e.g., multilingual-BERT models are widely used in cross-lingual tasks . |
| Approach: | They propose a language-agnostic approach to integrate universal syntax into language models . they use SYntax-aware networks and a COunterfactual training method . |
| Outcome: | The proposed model achieves state-of-the-art performance on natural language inference and question answering without auxiliary training data. |
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding (2023.findings-acl)
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| Challenge: | supervised methods for vision-language tasks have been well-studied, but they lack the fine-grained information needed for semantics understanding. |
| Approach: | They propose a framework to take advantage of fine-grained information for zero-shot vision-language learning, covering multiple tasks such as VQA, SNLI-VE, and VCR. |
| Outcome: | The proposed framework outperforms previous zero-shot methods on VQA and achieves substantial improvement on SNLI-VE and VCR. |
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023.findings-acl)
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| Challenge: | Recent work has shown that fine-tuning large language models on large instruction-following datasets improves their performance on a wide range of NLP tasks, but they fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task. |
| Approach: | They propose a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. |
| Outcome: | The proposed framework outperforms small LLMs on relation extraction (RE), a fundamental information extraction task, by a large margin. |
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
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| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
| Approach: | They propose a task that uses multilingual BERT representations of both the mention and context as input and explore zero-shot language transfer. |
| Outcome: | The proposed model performs well in both monolingual and multilingual settings. |
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation (2023.eacl-main)
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| Challenge: | Existing low-cost approaches to build a high-quality functioning dialogue agent are limited to a few widely-spoken languages. |
| Approach: | They propose automatic methods that use ToD training data to build a functioning agent in another language . they compare the method to existing methods that only use a small training set . |
| Outcome: | The proposed method improves the state-of-the-art in Chinese to English transfer using zero-shot data compared to existing full-shot methods . the proposed method achieves 46.7% and 22.0% in task success rate and dialogue success rate, respectively. |
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation (2025.findings-acl)
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Qianli Wang, Nils Feldhus, Simon Ostermann, Luis Felipe Villa-Arenas, Sebastian Möller, Vera Schmitt
| Challenge: | Existing frameworks for counterfactual examples are lacking for many tasks. |
| Approach: | They propose a faithful approach for leveraging important words from feature attribution methods to generate counterfactual examples in a zero-shot setting. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on many tasks. |
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning (2022.acl-short)
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| Challenge: | Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be effective for cross-lingual transfer of syntactic parsing models but only between related languages. |
| Approach: | They propose to use multi-task learning to dynamically optimize for parsing performance on outlier languages by using a multi-level learning approach. |
| Outcome: | The proposed method significantly outperforms uniform and size-proportional sampling in the zero-shot setting. |
CoLLaVO: Crayon Large Language and Vision mOdel (2024.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) and instruction tuning have been used to drive the evolution of Vision Language Model (VLM) towards a versatile general-purpose model. |
| Approach: | They propose a learning strategy of Dual QLoRA to preserve object-level image understanding without forgetting it during visual instruction tuning, thereby achieving a significant leap in numerous VL benchmarks in a zero-shot setting. |
| Outcome: | The proposed model outperforms closed-source models on vision language tasks and achieves a significant leap in numerous benchmarks. |
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training (2022.findings-emnlp)
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| Challenge: | Existing approaches require substantial adaptation of pretrained language models for vision-language reasoning tasks. |
| Approach: | They propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. |
| Outcome: | The proposed framework outperforms the Flamingo model on VQAv2 and GQA by 8.5%. |
From Citations to Criticality: Predicting Legal Decision Influence in the Multilingual Swiss Jurisprudence (2025.acl-short)
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| Challenge: | Existing approaches to evaluating the importance of legal cases are manual and resource-intensive. |
| Approach: | They propose a dataset that uses two-tier labels to evaluate case criticality . they use the LD-Label to identify cases published as Leading Decisions and the Citation-L Label to rank cases by their citation frequency and recency. |
| Outcome: | The Criticality Prediction dataset outperforms existing approaches to evaluate case criticality . the proposed model outperformed the existing models in a zero-shot setting . |
Revisiting LLMs as Zero-Shot Time Series Forecasters: Small Noise Can Break Large Models (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time series forecasting. |
| Approach: | They evaluate the effectiveness of LLMs as zero-shot forecasters compared to state-of-the-art domain-specific models by encoding sequences directly within prompts. |
| Outcome: | The proposed models perform well across multiple domains while reducing the need for domain-specific training. |
Learning to Paraphrase Sentences to Different Complexity Levels (2023.tacl-1)
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| Challenge: | Using unsupervised datasets, we train models on sentence complexification and same-level paraphrasing tasks. |
| Approach: | They compare two unsupervised datasets with a single supervised dataset to train models on sentence complexification and same-level paraphrasing tasks. |
| Outcome: | The proposed models outperform previous work on sentence-level targeting and improve on the ASSET simplification benchmark. |
Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study (2020.findings-emnlp)
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| Challenge: | Multilingual BERT (mBERT) has shown reasonable capability for zero-shot cross-lingual transfer when fine-tuned on downstream tasks. |
| Approach: | They propose to use parallel corpora and rotational alignment methods to improve transfer performance in a zero-shot setting. |
| Outcome: | The proposed method improves rotation-based alignment on Name Entity Recognition and Semantic Slot Filling tasks. |
TreeCut: A Synthetic Unanswerable Math Word Problem Dataset for LLM Hallucination Evaluation (2025.acl-short)
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| Challenge: | Large language models (LLMs) can achieve near-human performance on benchmarks like GSM8K, yet their true reasoning ability remains disputed. |
| Approach: | They propose a synthetic dataset that generates infinite unanswerable math word problems and their answerable counterparts by representing each question as a tree and removing selected necessary conditions. |
| Outcome: | Experiments show TreeCut induces hallucinations in large language models, including GPT-4o and o3-mini, with rates of 64% and 44% in worst-case scenarios. |
Selecting Key Views for Zero-Shot Entity Linking (2023.findings-emnlp)
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| Challenge: | Entity linking is a task of assigning ambiguous mentions in textual input to entities in knowledge bases. |
| Approach: | They propose a framework to align mentions in text to entities in knowledge bases . they use unsupervised clustering to select key views from descriptions . |
| Outcome: | The proposed framework achieves state-of-the-art on the zero-shot entity linking dataset. |
Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models (2024.eacl-long)
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| Challenge: | Pretrained language models learn cross-lingual knowledge and perform well on diverse tasks when finetuned. |
| Approach: | They propose a zero-shot prompting approach that captures cross-lingual word sense with a contextual prompt. |
| Outcome: | The proposed approach outperforms baselines on recall in many evaluation languages without additional training or finetuning. |
Toward Informal Language Processing: Knowledge of Slang in Large Language Models (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have offered a strong potential for natural language systems to process informal language. |
| Approach: | They propose to use movie subtitles to evaluate slang in large language models . they find that smaller LLMs finetuned on the dataset achieve comparable performance . |
| Outcome: | The proposed dataset can be used to evaluate LLMs on slang detection and identification of regional and historical sources for interpretive insights. |
Virtual Knowledge Graph Construction for Zero-Shot Domain-Specific Document Retrieval (2022.coling-1)
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| Challenge: | Domain-specific documents cover terminologies and specialized knowledge. |
| Approach: | They propose a domain-specific document retrieval method that embeds a document into a graph of entities and their relations into . they compare the unsupervised method with previous approaches and use it to compute relevance between queries and documents. |
| Outcome: | The proposed method outperforms baselines and fully-supervised bi-encoders in a zero-shot setting and outperformed bi-supervised approaches. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
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| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
SelectNoise: Unsupervised Noise Injection to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages (2023.findings-emnlp)
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| Challenge: | Currently, MT systems for low-resource languages lack parallel data and monolingual data. |
| Approach: | They propose an unsupervised approach to generate noisy HRLs training data by selective candidate extraction and noise injection. |
| Outcome: | The proposed model outperforms strong baselines on 12 ELRLs in a zero-shot setting . |
SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes (2026.acl-long)
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| Challenge: | Existing approaches depend on static, pre-processed database information, which restricts the model’s capacity to deeply comprehend the underlying database content. |
| Approach: | They propose a framework that empowers LLMs to perform Self-Driven Exploration of databases during inference. |
| Outcome: | Evaluated on the BIRD benchmark with Qwen2.5-72B-Instruct, SDE-SQL achieves an 8.02 % improvement in execution accuracy over the baseline. |
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances (2025.findings-naacl)
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| Challenge: | Recent studies have demonstrated that Large Language Models possess a form of emotional intelligence, capable of interpreting emotional stimuli in text. |
| Approach: | They propose a method that translates speech characteristics into natural language descriptions and integrates them into LLMs to perform multimodal emotion analysis via text prompts. |
| Outcome: | The proposed method outperforms baseline models that require structural modifications on two datasets showing significant improvements in emotion recognition accuracy. |
No Innocence in Styling: Discovery of Privacy Protection Capabilities and Security Risks in Consumer Generative AI Writing Assistants (2026.acl-industry)
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Mohd. Farhan Israk Soumik, Syed Mhamudul Hasan, Wanniarachchi Kankanamge Malithi Mithsara, Ahmed Imteaj, Abdur R. Shahid
| Challenge: | a recent study examines the dual-use nature of platform-level text stylization. |
| Approach: | They examine the dual-use nature of platform-level text stylization by examining their implications for privacy and platform safety. |
| Outcome: | The proposed model reduces emotion inference accuracy, lowers profiling risk, and increases error rates in misinformation detection. |
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck (2024.findings-naacl)
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| Challenge: | CLIP-based classifiers rely on the prompt containing a class name that is known to the text encoder and perform poorly on new classes or the classes whose names rarely appear on the Internet. |
| Approach: | They propose to use a set of text descriptors to express a class name into a textual descriptable and match the embeddings of the detected parts to their textual ones to compute a logit score. |
| Outcome: | The proposed classifier outperforms CLIP-based classifiers on zero-shot and supervised learning settings by 88.80% and 92.20% accuracy on CUB-200 and Stanford Dogs-120. |
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to data-to-text generation require limited training examples . a data-based approach is based on a set of pre-trained language models with optional finetuning. |
| Approach: | They propose a data-to-text generation task that makes use of any given (or no) examples. |
| Outcome: | The proposed approach improves on baselines on a dataset with zero/few/full-shot settings. |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
| Challenge: | Large pre-trained models have improved performance on a variety of natural language processing tasks. |
| Approach: | They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators. |
| Outcome: | The proposed model performs better on two NL-PL applications by fine-tuning model parameters. |
What Is Missing in Multilingual Visual Reasoning and How to Fix It (2025.findings-naacl)
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| Challenge: | NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users. |
| Approach: | They propose a translation-test approach to tackle multilinguality, visual programming approach to break down complex reasoning, and a method that leverages image captioning to address multimodality. |
| Outcome: | The proposed interventions boost open models LLaVA-v1.5-13B by 13.4%, LLva-v1.6-34B by 20.3%, and Qwen-VL by 16.7% while minorly improving GPT-4V’s performance. |
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)
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Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan
| 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. |
LazyReview: A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews (2025.acl-long)
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| Challenge: | Large Language Models struggle to detect lazy thinking in a zero-shot setting, but instruction-based fine-tuning significantly boosts performance by 10-20 performance points. |
| Approach: | They propose to use LazyReview to train junior reviewers in the community to detect lazy thinking in peer-review sentences annotated with fine-grained lazy thinking categories. |
| Outcome: | The proposed dataset shows that LLMs struggle to detect lazy thinking instances in a zero-shot setting, while instruction-based fine-tuning significantly boosts performance by 10-20 performance points. |
Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
| Approach: | They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%. |
SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)
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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu
| Challenge: | Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era. |
| Approach: | They propose a framework that integrates structured representations into LLMs from training-free and training-dependent perspectives. |
| Outcome: | The proposed framework integrates structured representations through natural language descriptions in LLM prompts while augmenting the model’s inference capability through fine-tuning on linguistically described structured representation. |
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization (2025.naacl-long)
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| Challenge: | Machine Translation (MT) systems based on fine-tuned large language models (LLMs) are at a higher risk of generating hallucinations, which can severely undermine user’s trust and safety. |
| Approach: | They propose a method that intrinsically learns to mitigate hallucinations during the model training phase. |
| Outcome: | The proposed method reduces hallucinations by 89% on an average across three unseen target languages while preserving translation quality. |
Context-Aware Answer Extraction in Question Answering (2020.emnlp-main)
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| Challenge: | Extractive QA models have shown promising performance in predicting the correct answer to a given question. |
| Approach: | They propose a BLANC-based context prediction task that learns the context prediction tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art models on reading comprehension and hotpotQA. |
Prompt Consistency for Zero-Shot Task Generalization (2022.findings-emnlp)
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| Challenge: | Recent work has shown that pre-trained language models can perform zero-shot generalization to new tasks without annotated examples. |
| Approach: | They propose to regularize prompt consistency to encourage consistent predictions over a diverse set of prompts. |
| Outcome: | The proposed approach outperforms the state-of-the-art zero-shot learner, T0, on 9 out of 11 datasets across 4 NLP tasks by 10.6 absolute points in terms of accuracy. |
Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs (2023.findings-emnlp)
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| Challenge: | Currently, there are almost 150,000 Unicode characters, which presents extensive substitution possibilities. |
| Approach: | They develop a character substitution scraping method to collect hate speech . they use an annotated dataset with 1,281 non-Latin characters to scrape out offensive words . |
| Outcome: | The proposed method can detect hate speech with annotated data, but it performs poorly in a zero-shot setting. |
Learning to Retrieve Passages without Supervision (2022.naacl-main)
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| Challenge: | Dense retrievers for open domain question answering have been shown to achieve impressive performance by training on large datasets of question-passage pairs. |
| Approach: | They propose to use recurring spans to create pseudo examples for contrastive learning. |
| Outcome: | The proposed model outperforms all pretrained baselines on a wide range of ODQA datasets and is competitive with BM25, a strong sparse baseline. |
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models (2021.naacl-main)
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| Challenge: | a new study examines zero-shot cross-lingual transfer of vision-language models . we study multilingual text-to-video search in non-English languages without annotations . |
| Approach: | They propose a Transformer-based model that learns contextual multilingual multimodal embeddings . they propose 'zero-shot cross-lingual transfer' to improve multilingual search . |
| Outcome: | The proposed model outperforms baselines on multilingual text-to-video search and multilingual image search on VTT and VATEX. |
Biomedical Concept Normalization over Nested Entities with Partial UMLS Terminology in Russian (2024.lrec-main)
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| Challenge: | Existing annotations in Russian do not include all entities, but only a small fraction of them are labeled in English. |
| Approach: | They present a manually annotated PubMed abstract dataset for concept normalization in Russian. |
| Outcome: | The proposed model improves on nested named entities in a zero-shot setting on bilingual terminology. |
Better Zero-Shot Reasoning with Self-Adaptive Prompting (2023.findings-acl)
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| Challenge: | Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. |
| Approach: | They propose a new method that uses a set of examples from the LLM zero-shot outputs to improve performance. |
| Outcome: | The proposed method improves performance up to 15% compared to baselines and matches or exceeds few-shot baselines at a range of reasoning tasks. |
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models (2024.naacl-long)
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| Challenge: | Currently, large language models (LLMs) train on short text segments due to the computational overhead quadratic in the input lengths of their Transformer architectures. |
| Approach: | They propose a method that allows LLMs pre-trained with 2K or 4K-long segments to generalize to up to 200M length inputs while retaining perplexity. |
| Outcome: | The proposed method achieves 2.7 decoding speed up and 7.5 memory saving over the original model. |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
Better Zero-Shot Reasoning with Role-Play Prompting (2024.naacl-long)
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Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong
| Challenge: | Recent years have witnessed a paradigm shift in natural language processing, driven by large language models such as GPT-3, PaLM, and Llama. |
| Approach: | They propose a strategy for role-play prompting and assess its performance under the zero-shot setting. |
| Outcome: | The proposed method outperforms the standard zero-shot prompting approach across 12 reasoning benchmarks. |
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis (2024.naacl-long)
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| Challenge: | Existing datasets for hate speech detection neglect the cultural diversity within a single language. |
| Approach: | They propose a CR**oss-cultural **E**nglish **Hate* speech dataset that uses culturally hateful keywords to identify posts from four countries plus the United States. |
| Outcome: | The proposed dataset shows that only 56.2% of the posts in CREHate achieve consensus among all countries, with the highest pairwise label difference rate of 26%. |
Missci: Reconstructing Fallacies in Misrepresented Science (2024.acl-long)
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| Challenge: | False or misleading narratives spread rapidly on social networks, posing challenges for non-experts in discerning credible information. |
| Approach: | They propose a model for fallacious reasoning that focuses on implicit fallacies between relevant content and the inaccurate claim and requires models to verbalize the fallacious thinking in addition to classifying it. |
| Outcome: | The proposed model focuses on implicit fallacies between relevant content and the inaccurate claim and requires models to verbalize the fallacious reasoning in addition to classifying it. |
ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)
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| Challenge: | Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora. |
| Approach: | They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers. |
| Outcome: | The proposed model improves in-domain and cross-domain performance on children's speech. |
Investigating the Emergent Audio Classification Ability of ASR Foundation Models (2024.naacl-long)
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| Challenge: | Text and vision foundation models can perform many tasks in a zero-shot setting . however, there has been little work on the zero-shoot abilities of ASR foundation models . |
| Approach: | They investigate the ability of ASR foundation models to perform zero-shot audio classification using text prompts and a decoding probability generator. |
| Outcome: | The proposed model outperforms state-of-the-art models on audio classification datasets without training them on extra data or adding any parameters. |
Pattern Recognition or Medical Knowledge? The Problem with Multiple-Choice Questions in Medicine (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are often evaluated using multiple-choice questions (MCQs) modeled on exams like the USMLE. |
| Approach: | They created a fictional medical benchmark centered on an imaginary organ, the Glianorex, to separate memorized knowledge from reasoning ability. |
| Outcome: | The proposed model outperforms base models in English but not in French. |
Semantically-Prompted Language Models Improve Visual Descriptions (2024.findings-naacl)
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| Challenge: | Language-vision models have made significant progress in zeroshot vision tasks, but lack expressive visual descriptions. |
| Approach: | They propose a new method for generating visual descriptions with pre-trained language models and semantic knowledge bases. |
| Outcome: | The proposed method improves visual descriptions and achieves strong results on image-classification datasets. |
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback (2022.findings-emnlp)
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| Challenge: | Recent work on dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language models (PLMs). |
| Approach: | They propose a progressive zero-shot dataset generation framework which leverages feedback from the task-specific model to guide the generation of new training data via in-context examples. |
| Outcome: | The proposed framework achieves on-par or superior performance with only 1% synthetic dataset size, when compared to baseline methods without in-context feedback. |
Joint Multilingual Supervision for Cross-lingual Entity Linking (D18-1)
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| Challenge: | Entity Linking (XEL) systems ground entity mentions written in any language to Wikipedia . XEL is challenging for most languages due to limited availability of resources as supervision . |
| Approach: | They develop a cross-lingual XEL approach that combines supervision from multiple languages jointly. |
| Outcome: | The proposed approach significantly improves on the current state-of-the-art in 8 languages. |
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models (2023.eacl-main)
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| Challenge: | Recent work aimed to improve task performance of large language models by rewriting or tuning them manually, but manual rewrite is time-consuming and requires subjective interpretation. |
| Approach: | They propose a gradient-free, edit-based search approach for improving task instructions for large language models. |
| Outcome: | The proposed approach outperforms manual rewriting and purely example-based prompts while allowing for API-based tuning. |
Planning First, Question Second: An LLM-Guided Method for Controllable Question Generation (2024.findings-acl)
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| Challenge: | Current CQG methods focus on difficulty control, neglecting the control of question content and assessed abilities, which are also crucial in educational QG. |
| Approach: | They propose an LLM-guided method PFQS which utilizes Llama 2 to generate an answer plan and then generates questions based on it. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves better consistency with requirements in a zero-shot setting. |
M3: Multi-level dataset for Multi-document summarisation of Medical studies (2022.findings-emnlp)
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| Challenge: | Existing summarisation systems are not up to such complex tasks, yet limited tools exist to determine where and why they are failing. |
| Approach: | They propose to use a dataset to evaluate the quality of summarisation systems in the biomedical domain. |
| Outcome: | The proposed model can be used to evaluate the quality of summarisation systems in the biomedical domain. |
Towards Zero-shot Language Modeling (D19-1)
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| Challenge: | a number of natural questions have been asked about the inductive biases of neural networks on core NLP tasks. |
| Approach: | They construct an informative prior for held-out languages on a task of character-level, open-vocabulary language modelling. |
| Outcome: | The proposed model outperforms baseline models with an uninformative prior in both zero-shot and few-shot settings, showing that it is imbued with universal linguistic knowledge. |
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies (2024.findings-emnlp)
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| Challenge: | CultureBank is a knowledge base built upon users’ self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. |
| Approach: | They construct a pipeline to construct cultural knowledge bases from different online communities on a massive scale. |
| Outcome: | The proposed pipeline improves cultural awareness of language models by evaluating them on two cultural tasks in a zero-shot setting. |
On the Calibration of Massively Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Massively Multilingual Language Models (MMLMs) have gained popularity due to their effectiveness in cross-lingual transfer. |
| Approach: | They investigate how well calibrated MMLMs are with respect to confidence . they find that calibration methods like temperature scaling and label smoothing improve calibration . |
| Outcome: | The proposed models are able to generalize in languages unseen during fine-tuning, but they are not reliable across languages. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering (2021.findings-acl)
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| Challenge: | Disfluencies are an under-studied topic in NLP, even though it is ubiquitous in human conversation. |
| Approach: | They propose a challenge question answering dataset where humans introduce contextual disfluencies in previously fluent questions. |
| Outcome: | The proposed dataset shows that existing models degrade significantly when tested on DISFL-QA in a zero-shot setting. |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
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| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge (2021.emnlp-main)
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| Challenge: | Recent success of neural language models on the Winograd Schema Challenge has called for further investigation of commonsense reasoning ability of these models. |
| Approach: | They propose a logic-based framework that focuses on high-quality commonsense knowledge. |
| Outcome: | The proposed framework focuses on high-quality commonsense knowledge. |
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? (2024.findings-emnlp)
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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
| Challenge: | Numerical data is pivotal for medical questions and answers, but tabular data is not fully integrated into LLMs. |
| Approach: | They examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record data. |
| Outcome: | The proposed representations outperform those using raw numerical EHR data in medical diagnostics and prognostics. |
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts (2023.acl-long)
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| Challenge: | Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. |
| Approach: | They propose to use few-shot learning settings to fine-tune the sentiment classification model using manual or automatically generated prompts. |
| Outcome: | The proposed method outperforms the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task. |
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
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| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |
DeVisE: Towards the Behavioral Testing of Medical Large Language Models (2026.findings-eacl)
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| Challenge: | Existing evaluations of large language models do not reveal whether their outputs reflect genuine medical reasoning or superficial correlations. |
| Approach: | They propose a framework that probes fine-grained clinical understanding through controlled counterfactuals. |
| Outcome: | The proposed framework is based on demographic and vital signs data from the ICU discharge notes of patients in the intensive care unit (MIMIC-IV). |
Context Matters: Enhancing Metaphor Recognition in Proverbs (2024.lrec-main)
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| Challenge: | Figurative language interpretation requires models to navigate beyond literal meaning and delve into underlying semantics of the figurative expressions. |
| Approach: | They propose to use GPT-3.5 to perform word-level metaphor detection in a zero-shot setting to examine its performance. |
| Outcome: | The proposed model performs well in identifying word-level metaphors in English proverbs in zero-shot setting. |
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)
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| Challenge: | Existing meta-path generation methods cannot fully exploit rich textual information in HINs. |
| Approach: | They propose a text-infilling-based approach to generate meta-paths from textual information in HINs. |
| Outcome: | The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths. |
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting. |
| Approach: | They propose a task-oriented parsing method that decomposes parse problem into abstractive and extractive question-answering problems. |
| Outcome: | The proposed method decomposes a parsing problem into abstractive and extractive question-answering (QA) problems. |
The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models (2023.emnlp-main)
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| Challenge: | Pre-trained language-and-vision models have impressive performance in downstream tasks, but it remains unclear whether this improves understanding of image-text interaction. |
| Approach: | They propose to use BLA to evaluate multimodal models on basic linguistic constructions that even preschool children can typically master. |
| Outcome: | The proposed model improves basic language skills in a zero-shot learning setting. |
Monolingual Adapters for Zero-Shot Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing adapter layers are more parameter-efficient and provide better performance than bilingual ones. |
| Approach: | They propose to use monolingual adapter layers instead of bilingual ones to compose them and generalize to unseen language pairs. |
| Outcome: | The proposed adapter layer formalism achieves a median improvement of +2.77 BLEU points over a 20-language multilingual Transformer baseline trained on TED talks. |
MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms (2024.findings-acl)
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| Challenge: | Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. |
| Approach: | They propose a benchmark to evaluate MLLMs' understanding of multimodal social media content and a large-scale YouTube tagging dataset to evaluate their performance. |
| Outcome: | The proposed model performs better in a zero-shot setting, suggesting potential improvements. |
Zero-shot Disfluency Detection for Indian Languages (2022.coling-1)
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| Challenge: | Disfluency correction models can help alleviate this problem, but the unavailability of labeled data in low-resource languages impairs progress. |
| Approach: | They propose to use a pretrained multilingual model to detect zero-shot disfluency in Indian languages. |
| Outcome: | The proposed model achieves F1 scores of 75 and higher on five disfluency types across four languages. |
On the Relation between Syntactic Divergence and Zero-Shot Performance (2021.emnlp-main)
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| Challenge: | Recent advances in cross-lingual transfer methods have enabled significant advances in grammatical processing tasks. |
| Approach: | They examine the extent to which syntactic relations are preserved in translation and parsability in a zero-shot setting. |
| Outcome: | The proposed model is based on a translation task in English and a subset of a standard English RE benchmark translated to Russian and Korean. |
BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla (2025.findings-naacl)
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Fabiha Haider, Fariha Tanjim Shifat, Md Farhan Ishmam, Md Sakib Ul Rahman Sourove, Deeparghya Dutta Barua, Md Fahim, Md Farhad Alam Bhuiyan
| Challenge: | Existing work on monolingual or binary hate classification in Bangla has not addressed the challenge of multi-label hate speech classification in underrepresented languages. |
| Approach: | They propose a multi-label transliterated Bangla hate speech dataset that translates or transliterates under-resourced text to higher-resource text before classifying the hate group(s). |
| Outcome: | The proposed approach outperforms other methods in the zero-shot setting while achieving state-of-the-art performance. |
AutoEQA: Auto-Encoding Questions for Extractive Question Answering (2021.findings-emnlp)
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| Challenge: | Extractive question answering models are reliant on annotations of answer-spans in the corresponding passages. |
| Approach: | They propose a method that auto-encodes a question and generates corresponding questions from it. |
| Outcome: | The proposed method performs well in a zero-shot setting and can provide an additional loss to boost performance for extractive question answering (EQA). |
PROST: Physical Reasoning about Objects through Space and Time (2021.findings-acl)
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| Challenge: | a new dataset is available to test pretraining of physical reasoning models . state-of-the-art models are inadequate at reasoning about physical interactions, authors say . |
| Approach: | They present a dataset that contains 18,736 multiple-choice questions from 14 templates . they propose to use the dataset to probe both causal and masked language models . |
| Outcome: | The proposed dataset contains 18,736 multiple-choice questions covering 10 physical reasoning concepts. |
GLiREL - Generalist Model for Zero-Shot Relation Extraction (2025.naacl-long)
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| Challenge: | Existing approaches to zero-shot named entity recognition rely on distant supervision and training data for unseen labels. |
| Approach: | They propose an efficient architecture and training paradigm for zero-shot relation classification . they use a protocol to generate multiple relation labels in a single forward pass . |
| Outcome: | The proposed architecture and training paradigm achieve state-of-the-art results on the zero-shot relation classification task. |
Leveraging LLMs for Bangla Grammar Error Correction: Error Categorization, Synthetic Data, and Model Evaluation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) perform exceedingly well in Natural Language Understanding tasks for many languages including English. |
| Approach: | They propose to use a rule-based noise injection method to create grammatically incorrect sentences . they categorize 12 error classes in Bangla and take a survey of native speakers . |
| Outcome: | The proposed method improves performance of LLMs in Bangla by 3-7 percentage points compared to zero-shot setting . human errors are still superior in error correction, the authors show . |
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages (2022.acl-long)
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Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann
| Challenge: | Pretrained multilingual models can perform cross-lingual transfer in a zero-shot setting, even for unseen languages. |
| Approach: | They propose to extend XNLI to 10 indigenous languages of the Americas and test multiple zero-shot and translation-based approaches. |
| Outcome: | The proposed model can perform cross-lingual transfer in a zero-shot setting even for languages unseen during pretraining. |
A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)
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| Challenge: | Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability. |
| Approach: | They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor. |
| Outcome: | The proposed method outperforms baseline methods by 6%-16% in F1 scores. |
Boosting Natural Language Generation from Instructions with Meta-Learning (2022.emnlp-main)
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| 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" |
ChartAssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning (2024.findings-acl)
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| Challenge: | Charts are an effective tool for understanding data patterns, but their combination of graphical elements and textual components poses challenges for general-purpose multimodal models. |
| Approach: | They propose a chart-based vision-language model for universal chart comprehension and reasoning that leverages a dataset of chart-related tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art charts with zero-shot setting on various chart tasks. |
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents (2023.findings-acl)
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| Challenge: | Xu et al., 2020 focus on semi-structured document classification in a zero-shot setting . positional, layout, and style information play a vital role in interpreting such documents . |
| Approach: | They propose a matching-based approach that relies on a pairwise contrastive objective for pretraining and fine-tuning. |
| Outcome: | The proposed method significantly improves Macro F1 in the zero-shot learning setting. |
Separating Context and Pattern: Learning Disentangled Sentence Representations for Low-Resource Extractive Summarization (2023.findings-acl)
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| Challenge: | Context information is one of the key factors for extractive summarization, but other factors can be used to identify sentence importance. |
| Approach: | They propose to disentangle context and pattern factors for extractive summarization . they separate context and patterns for a better generalization ability in low-resource setting . |
| Outcome: | The proposed model can be used in the zero-shot setting or fine-tuned in the few-shot settings. |
Task-Agnostic Low-Rank Adapters for Unseen English Dialects (2023.emnlp-main)
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| Challenge: | a recent study found that LLMs are trained on corpora disproportionally weighted in favor of Standard American English . prior work on dialect struggle with generalizing to evolving and emerging dialects in a scalable manner. |
| Approach: | They propose a method that leverages linguistic knowledge to enable resource-efficient adaptation . their method disentangles dialect-specific and cross-dialectal information . |
| Outcome: | a new method improves generalization to unseen dialects in a task-agnostic fashion . it achieves the best or most competitive performance across 5 dialects . |
Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision–Language Models (2025.naacl-long)
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| Challenge: | a new study shows that cultural background significantly affects multimodal hate speech moderation models . a limited dataset excludes multi-modal forms of hate and excludes non-English-speaking cultures . the lowest pairwise label agreement between the USA and India is due to cultural factors . |
| Approach: | They use a multimodal and multilingual parallel hate speech dataset to examine cultural differences . they find that cultural background significantly affects multimodal hate speech annotation . |
| Outcome: | The proposed dataset shows that cultural background significantly affects multimodal hate speech annotation. |
ELLEN: Extremely Lightly Supervised Learning for Efficient Named Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) tasks require an amount of annotations that are unrealistic for many real-world applications. |
| Approach: | They propose a semi-supervised named entity recognition method that blends language models with linguistic rules. |
| Outcome: | The proposed method outperforms most existing semi-supervised methods under the same supervision settings commonly used in the literature. |
POSQA: Probe the World Models of LLMs with Size Comparisons (2023.findings-emnlp)
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| Challenge: | Embodied language comprehension emphasizes that language understanding is not only mental processing in the brain but also involves interactions with the physical and social environment. |
| Approach: | They propose to use a physical object size question to examine the extremity of large language models to test their embodied comprehension. |
| Outcome: | The proposed dataset shows that even the largest LLMs perform poorly under the zero-shot setting. |
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue (2022.findings-emnlp)
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| Challenge: | Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. |
| Approach: | They propose a modular model for incorporating knowledge into conversational agents that generates a knowledge sequence and then attends to its own generated knowledge sequence. |
| Outcome: | The proposed model hallucinates less in knowledge-grounded dialogue tasks and has advantages in terms of interpretability and modularity. |
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at straightforward reasoning tasks, but struggle when faced with complex multi-step reasoning. |
| Approach: | They propose a framework that converts unstructured text into a graph and instructs LLMs to navigate this graph using task-specific strategies. |
| Outcome: | The proposed framework improves the multi-step reasoning capabilities of Large Language Models in a zero-shot setting. |
InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models (2024.findings-acl)
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| Challenge: | Existing sentences do not consider the length constraints in extractive summarization because of their limited model abilities. |
| Approach: | They propose an approach that incorporates length constraints without model modifications into sentences . they use traditional sentence compression datasets to transform them into instruction format . |
| Outcome: | The proposed method can consider the length constraint through instructions without model modifications. |
Quantifying the Persona Effect in LLM Simulations (2024.acl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. |
| Approach: | They investigate how integrating persona variables—demographic, social, and behavioral factors—impacts LLMs’ ability to simulate diverse perspectives. |
| Outcome: | The proposed model improves on a zero-shot model with persona prompting. |
Understanding LLMs’ summarization capabilities: an analysis of biomedical abstract and lay summary generation (2026.findings-acl)
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| Challenge: | Abstracts use technical language for academic audiences, while lay summaries aim to make findings accessible to non-specialists. |
| Approach: | They evaluate the performance of lightweight LLMs in generating biomedical abstracts and lay summaries in a zero-shot setting. |
| Outcome: | The proposed models perform well in generating biomedical abstracts and lay summaries in a zero-shot setting. |
Language Embeddings for Typology and Cross-lingual Transfer Learning (2021.acl-long)
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
| Outcome: | The proposed model can be leveraged in cross-lingual tasks without parallel data . the proposed model is based on the World Atlas of Language Structures (WALS) and two extrinsic tasks . |
A Multi-task Learning Framework for Quality Estimation (2023.findings-acl)
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Sourabh Deoghare, Paramveer Choudhary, Diptesh Kanojia, Tharindu Ranasinghe, Pushpak Bhattacharyya, Constantin Orăsan
| Challenge: | Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level and document-level . |
| Approach: | They propose to train a single model for sentence-level and word-level QE tasks in a multi-task learning framework and compare them to baseline models. |
| Outcome: | The proposed model improves on the single-pair, multi-patch, and zero-shot settings. |
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms (2021.emnlp-main)
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| Challenge: | Pre-trained language models have impressive performance on commonsense inference benchmarks, but their ability to make robust inferences is debated. |
| Approach: | They propose a challenge that evaluates robust commonsense inference despite textual perturbations using commonsensical knowledge bases and probe PTLMs across two different evaluation settings. |
| Outcome: | The proposed procedure evaluates robust commonsense inference despite textual perturbations using commonsensense knowledge bases and probe PTLMs across two evaluation settings. |
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model (D19-1)
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| Challenge: | Existing studies on RC datasets in English have limited results due to lack of training data. |
| Approach: | They systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with pre-trained language representation model. |
| Outcome: | The proposed model performs well on reading comprehension tasks on pre-trained language representation models. |
ProTOD: Proactive Task-oriented Dialogue System Based on Large Language Model (2025.coling-main)
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| Challenge: | Existing task-oriented dialogue systems engage with users in a reactive manner, relying on a basic single-query mechanism and employing passive policy planning. |
| Approach: | They propose a novel LLM-based proactive TOD framework to improve system proactivity and goal completion. |
| Outcome: | The proposed framework improves system proactivity and goal completion rates by 10% while enhancing proactive engagement. |
A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering (2025.findings-emnlp)
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| Challenge: | Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) KGQA methods based on LLMs are limited in their ability to model KG structure without additional data. |
| Approach: | They propose a KGQA framework that can operate in a zero-shot setting . they propose NS-KGQA to use neural KG embeddings to model KG structure . |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot baselines by 26%. |
On the Role of Semantic Proto-roles in Semantic Analysis: What do LLMs know about agency? (2025.findings-acl)
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| Challenge: | Existing studies on large language models (LLMs) have not explored their capacity to reason over event structure . et al., 2015, 142: e007-e0027; eugene, 1985; Weiner, 1995; saab, 1985) focus on the role of large language model in decision-making . |
| Approach: | They propose to characterize agents via properties such as "instigation" and "volition" they also examine whether incorporating semantic proto-role labeling context improves SRL performance . |
| Outcome: | The proposed model improves in a zero-shot setting by incorporating proto-role labeling context . the results support previous work showing that LLMs underperform human annotators in complex semantic analysis. |
Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations (2024.emnlp-main)
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| Challenge: | Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. |
| Approach: | They propose a methodological pipeline to investigate model performance across structural attributes of conversations. |
| Outcome: | The proposed method analyzes the performance of an LLM to classify multi-party conversations . it shows that response selection relies more on the textual content of conversations compared to addressee recognition . |
Evaluating and Improving Factuality in Multimodal Abstractive Summarization (2022.emnlp-main)
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| Challenge: | Current factuality metrics do not account for vision modality, thus are not adequate for vision-and-language summarization. |
| Approach: | They propose a weighted combination of CLIPScore and BERTScore to evaluate factuality for abstractive document summarization. |
| Outcome: | The proposed metric outperforms existing factuality metrics on four factuity metric-evaluation benchmarks and is robust to human judgments. |
LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments (2025.acl-long)
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| Challenge: | Existing work on IR focus on retrieving entire cases rather than precise, paragraph-level information. |
| Approach: | They propose a cross-lingual dataset for paragraph-level retrieval from ECtHR judgments . they evaluate retrieval models in a zero-shot setting and use multilingual case law guides . |
| Outcome: | The proposed model excels in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. |
GPT-3.5 for Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Recent work shows that GPT-3.5 struggles with several error types, including punctuation mistakes, tense errors, syntactic dependencies between words, and lexical compatibility at the sentence level. |
| Approach: | They evaluate GPT-3.5 for grammatical error correction in multiple languages . they use it to re-rank correction hypotheses generated by other GEC models . |
| Outcome: | The proposed model performs well in English and Russian, but struggles with errors in other languages. |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data-to-text generation rely on labeled data, which is costly to acquire and limits their application to new tasks and domains. |
| Approach: | They propose to leverage pre-training and transfer learning to address this problem by leveraging a general knowledge-grounded generation model and a knowledge-based model. |
| Outcome: | The proposed model can generate knowledge-enriched text on a knowledge-grounded text corpus crawled from the web in three settings. |
Android in the Zoo: Chain-of-Action-Thought for GUI Agents (2024.findings-emnlp)
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| Challenge: | Existing studies on large language models (LLMs) focus on the semantics of smartphone operations. |
| Approach: | They propose a large language model (LLM) which predicts a sequence of actions of API by analyzing past actions and visual observations. |
| Outcome: | The proposed model improves the prediction of actions on a zero-shot Android-In-The-Zoo dataset compared to previous models . |
An LLM-Enabled Knowledge Elicitation and Retrieval Framework for Zero-Shot Cross-Lingual Stance Identification (2024.findings-emnlp)
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| Challenge: | Existing research is conducted in monolingual setting on English datasets, whereas in other low-resource languages, it lacks sufficient data for training quality stance detection models. |
| Approach: | They propose a knowledge elicitation and retrieval framework that leverages the capability of large language models for stance knowledge acquisition and matches the target language input to the most relevant stance information. |
| Outcome: | The proposed framework improves on multilingual datasets and competitive baselines. |
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data (2023.findings-acl)
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| Challenge: | Structure Aware Dense Retrieval (SANTA) model encodes user queries and structured data in one universal embedding space for retrieving structured data. |
| Approach: | They propose to use structured data and unstructured data to encode queries and structured data in one universal embedding space for retrieving structured data. |
| Outcome: | The proposed model achieves state-of-the-art on code search and product search and conducts convincing results in the zero-shot setting. |
IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models (2023.findings-emnlp)
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Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad Ayyubi, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing approaches to decompose VL reasoning rely on domain-specific sub-question decomposing models. |
| Approach: | They propose a framework that iteratively decomposes VL reasoning using large language models. |
| Outcome: | The proposed framework outperforms existing models on multiple VL reasoning tasks. |
Generate then Refine: Data Augmentation for Zero-shot Intent Detection (2024.findings-emnlp)
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| Challenge: | Existing data augmentation methods rely on few labelled examples for each intent category, which can be expensive in settings with many possible intents. |
| Approach: | They propose a data augmentation method for intent detection in zero-resource domains by using an open-source large language model and a smaller sequence-to-sequence model. |
| Outcome: | The proposed method significantly improves the data utility and diversity over the zero-shot LLM baseline for unseen domains and over common baseline approaches. |
Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker (2023.acl-long)
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| Challenge: | Empirical results show plug-and-play approach to reason about belief states of multiple characters in reading comprehension tasks is more precise and interpretable than previous approaches. |
| Approach: | They propose a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation. |
| Outcome: | The proposed algorithm improves theory of mind of off-the-shelf neural language models without supervision. |
Generating Structured Pseudo Labels for Noise-resistant Zero-shot Video Sentence Localization (2023.acl-long)
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| Challenge: | Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal. |
| Approach: | They propose a Structure-based Pseudo Label generation (SPL) that generates free-form interpretable pseudo queries before constructing query-dependent event proposals. |
| Outcome: | The proposed method learns with only video data without any annotation . it generates free-form interpretable pseudo queries before constructing query-dependent event proposals . |
Nonparametric Decoding for Generative Retrieval (2023.findings-acl)
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Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, Minjoon Seo
| Challenge: | Existing text retrieval models depend on the information encoded in its parameters without external memory, its information capacity is limited and fixed. |
| Approach: | They propose a nonparametric decoding approach which uses external memory instead of vanilla vocab embeddings as decoder voka embedds. |
| Outcome: | The proposed model can utilize parametric and nonparametric space. |
A Study into Investigating Temporal Robustness of LLMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are limited in their ability to process temporal information and perform tasks requiring temporal reasoning and factual knowledge. |
| Approach: | They propose to use eight time-sensitiverobustness tests to test the model's temporal robustness for user questions in the zero-shot setting. |
| Outcome: | The proposed tests improve the temporal QA performance by up to 55%. |
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema (2021.emnlp-main)
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| Challenge: | Pre-trained language models have boosted performance on some WS benchmarks, but the source of improvement is not clear. |
| Approach: | They propose a method that uses twin sentences for evaluation and two new baselines that account for artifacts in WS benchmarks. |
| Outcome: | The proposed evaluation method is suboptimal for the Winograd Schema . it uses twin sentences to account for commonsense reasoning abilities . |
Is Everything in Order? A Simple Way to Order Sentences (2021.emnlp-main)
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| Challenge: | Existing work on sentence ordering has focused on exploiting different categories of features like coreference clues. |
| Approach: | They propose a sentence ordering task as a conditional text-to-marker generation problem that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. |
| Outcome: | The proposed model performs well across 7 datasets in Perfect Match Ratio and Kendall’s tau. |
Linguistically Motivated Sign Language Segmentation (2023.findings-emnlp)
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| Challenge: | Sign language segmentation is a crucial task in sign language processing systems. |
| Approach: | They propose to combine two kinds of segmentation: segmentation into individual signs and segmentation to segment into phrases, larger units comprising several signs. |
| Outcome: | The proposed model is based on linguistic cues observed in sign language corpora and replaces the predominant IO tagging scheme with BIO taging to account for continuous signing. |
Exploring Lottery Prompts for Pre-trained Language Models (2023.acl-long)
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| Challenge: | Existing approaches to optimize pre-trained language models are expensive and slow to scale. |
| Approach: | They propose to search for instance-level lottery prompts and generalize them to unseen data . they validate the assumption that for every instance, there is almost always a lottery prompt that induces the correct prediction from the PLM . |
| Outcome: | The proposed method can achieve comparable results with other gradient-free and optimization-free baselines. |
Crosslingual Generalization through Multitask Finetuning (2023.acl-long)
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Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel
| Challenge: | Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. |
| Approach: | They apply multitask prompted finetuning to pretrained multilingual models and generate variants called BLOOMZ and mT0. |
| Outcome: | The proposed models can generalize to non-English languages that have never been seen before. |
LFED: A Literary Fiction Evaluation Dataset for Large Language Models (2024.lrec-main)
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| Challenge: | LFED is a literary fiction evaluation dataset for large language models that evaluate the capability of LLMs on the long fiction comprehension and reasoning. |
| Approach: | They propose a Literary Fiction Evaluation Dataset to evaluate LLMs' comprehension and reasoning on long fictions. |
| Outcome: | The proposed dataset evaluates the capability of large language models on the long fiction comprehension and reasoning. |
LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models (2024.lrec-main)
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| Challenge: | Existing benchmarks for comprehensively evaluating Chinese Large Language Models are insufficient. |
| Approach: | They propose a Large-scale, Holistic, and Multi-subject Knowledge Evaluation benchmark to evaluate Chinese Large Language Models. |
| Outcome: | The proposed benchmark measures the knowledge acquisition capabilities of Chinese Large Language Models across 75 subjects from primary school to professional certification exams. |
On the Generalization vs Fidelity Paradox in Knowledge Distillation (2025.findings-acl)
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| Challenge: | Knowledge distillation (KD) is a key technique for compressing large language models into smaller ones while preserving performance. |
| Approach: | They propose to use knowledge distillation to compress large language models into smaller ones while preserving performance. |
| Outcome: | The proposed technique improves the performance of smaller models by 10% while providing only marginal benefits for larger models. |
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data (2024.lrec-main)
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| Challenge: | Prior research on Twitter has provided positive evidence of its utility in developing supplementary health surveillance systems. |
| Approach: | They propose a framework to surveil public health, focusing on mental health outcomes by using tweets from 765 neighborhoods in the USA. |
| Outcome: | The proposed framework achieves the highest F1-score and accuracy over the previous framework, and extrapolates CDC’s estimates to proxy unreported neighborhoods. |
MedQA-SWE - a Clinical Question & Answer Dataset for Swedish (2024.lrec-main)
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| Challenge: | MedQA-SWE is a clinical question & answering dataset in Swedish . it was created from exams aimed at evaluating doctors’ clinical understanding and decision making . |
| Approach: | They propose to create a multiple choice, clinical question & answering (Q&A) dataset in Swedish consisting of 3,180 questions. |
| Outcome: | The proposed dataset includes 3,180 questions and is the first open-source clinical Q&A dataset in Swedish. |
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Query-focused Summarization (QfS) is a system that generates summaries from document(s) based on a query. |
| Approach: | They propose a Query-focused Summarization approach that uses a generalization of Reinforcement Learning (RL) for Natural Language Generation and a better semantic similarity reward. |
| Outcome: | The proposed approach improves on the ROUGE-L metric and in a benchmark dataset. |
Do It Yourself (DIY): Modifying Images for Poems in a Zero-Shot Setting Using Weighted Prompt Manipulation (2025.emnlp-main)
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| Challenge: | a novel method to enhance imagery in poetic language is proposed . weighted prompt manipulation is a new approach to enhance poetry images . current diffusion models struggle to interpret metaphorical language, symbolism, and nuanced themes. |
| Approach: | They propose a weighted prompt manipulation technique that modifies attention weights and text embeddings within diffusion models to enhance or suppress specific words' influence in the final generated image. |
| Outcome: | The proposed technique enhances or suppresses the influence of specific words in the final generated image, leading to semantically richer and more contextually accurate visualizations. |
How do LLMs’ Preferences Affect Event Argument Extraction? CAT: Addressing Preference Traps in Unsupervised EAE (2025.findings-acl)
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| Challenge: | Existing approaches to supervised EAE suffer from preference traps due to misalignments between prior knowledge, instructions, or output constraints and LLMs’ preferences. |
| Approach: | They propose an unsupervised EAE framework that handles LLMs' preference traps by targeting their prior knowledge and instructions. |
| Outcome: | The proposed framework matches the best DeepSeek-R1 API model with a significantly lower time cost. |
AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP (2025.findings-emnlp)
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| Challenge: | Large language models have shown remarkable progress in reasoning abilities and general natural language processing tasks, yet their performance on Arabic data remains underexplored. |
| Approach: | They compare reasoning-focused LLMs with deepSeek models across 15 Arabic NLP tasks . they use zero-shot, few-shot and fine-tuning to evaluate their capacity for linguistic reasoning . |
| Outcome: | The proposed models outperform strong models on Arabic datasets and are compared with other models. |
P5: Plug-and-Play Persona Prompting for Personalized Response Selection (2023.emnlp-main)
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| Challenge: | a plug-and-play persona prompting system can be used to generate personalized responses for real applications . a recent study shows that dialog context alone is insufficient for personalized response selection . |
| Approach: | They propose a plug-and-play persona prompting method that can be used in real applications . they show that the method performs well in the zero-shot setting . |
| Outcome: | The proposed method performs well in the zero-shot setting, and can be fine-tuned for even better performance. |
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing (2023.findings-emnlp)
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| Challenge: | CASENT predicts ultra-fine entities mentioned in text into types with calibrated confidence scores. |
| Approach: | They propose a model that predicts ultra-fine entities with calibrated confidence scores for entity typing. |
| Outcome: | The proposed model outperforms existing models in terms of F1 score and calibration error while achieving 50 times faster inference speed. |
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science (2024.lrec-main)
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Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song
| Challenge: | Existing instruction-tuned Large Language Models (LLMs) have impressive language understanding and the capacity to generate responses that follow specific prompts. |
| Approach: | They evaluate the zero-shot performance of two publicly accessible LLMs, ChatGPT and OpenAssistant, in the context of six Computational Social Science classification tasks. |
| Outcome: | The proposed LLMs perform better than state-of-the-art models on social science tasks. |
LLM Agents at the Roundtable: A Multi-Perspective and Dialectical Reasoning Framework for Essay Scoring (2025.findings-emnlp)
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| Challenge: | a new framework for automated essay scoring is needed to achieve multi-perspective understanding and judgment. |
| Approach: | They propose a roundtable essay scoring framework that performs precise and human-aligned scoring under a zero-shot setting. |
| Outcome: | The proposed framework outperforms previous zero-shot AES approaches by enabling collaboration among agents with diverse evaluation perspectives. |
PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization (2024.lrec-main)
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| Challenge: | Existing summarization strategies are abstractive and extractive, but are hard to control. |
| Approach: | They propose a PhRase-level cOpying Mechanism that enhances attention on n-grams and calculates an auxiliary loss for the copying prediction. |
| Outcome: | Empirical studies show that PROM improves copying accuracy and faithfulness on benchmarks. |
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting. |
| Approach: | They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting. |
| Outcome: | The proposed model outperforms existing methods under zero-shot setting on three datasets. |
VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding (2025.emnlp-main)
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Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, Liwen Zhang
| Challenge: | Existing benchmarks focus on text comprehension, but MLLMs lack the ability to integrate visual data over financial visuals. |
| Approach: | They evaluate 21 state-of-the-art multimodal large language models in a zero-shot setting . they use an annotated question–answer pair from eight common financial image modalities . |
| Outcome: | The new benchmark outperforms existing models but trailed financial experts by 14 percentage points. |
The ELCo Dataset: Bridging Emoji and Lexical Composition (2024.lrec-main)
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| Challenge: | Emoji-Lexical Composition dataset provides parallel annotations of emoji sequences corresponding to English phrases. |
| Approach: | They propose a dataset that offers parallel annotations of emoji sequences corresponding to English phrases. |
| Outcome: | The Emoji-Lexical Composition (ELCo) dataset offers parallel annotations of emoji sequences corresponding to English phrases. |
Are Stereotypes Leading LLMs’ Zero-Shot Stance Detection ? (2025.emnlp-main)
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| Challenge: | Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many tasks. |
| Approach: | They propose to annotate posts in pre-existing stance detection datasets with dialect or vernacular of a specific group and text complexity/readability to investigate whether these attributes influence the model’s stance detect decisions. |
| Outcome: | The proposed model exhibits significant stereotypes when performing stance detection tasks in a zero-shot setting. |
Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents (2025.emnlp-main)
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| Challenge: | Large Language Models have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations . however, it is not straightforward to apply these methods directly for a more real-world use case of visualizing data from long documents . |
| Approach: | They propose an unsupervised method for generating intent-based charts from documents . they propose an attribution-based metric that uses a structured textual representation of charts . |
| Outcome: | The proposed method outperforms baselines in terms of chart data accuracy and chart type over baselines. |
HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation (2026.acl-long)
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| Challenge: | Existing frameworks for LLM-as-a-judge use zero-shot setting without consulting any human input, which leads to low alignment, or fine-tune LLMs on labeled data, which requires a non-trivial number of samples. |
| Approach: | They propose a hypothesis-guided evaluation framework that uses a small corpus of human evaluations to generate more detailed rubrics for human judgments and incorporates a checklist-like approach to combine LLM’s assigned scores on each decomposed dimension to acquire overall scores. |
| Outcome: | The proposed framework outperforms existing frameworks in both human rankings and human scores with 30 human evaluations and fine-tunes LLMs on labeled data with 3 times more human evaluation by 11.95%. |
GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark (2026.findings-acl)
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| Challenge: | Authorship verification (AV) is a task of determining whether two texts were written by the same author. |
| Approach: | They propose a benchmark for German AV comprising over 400k labeled text pairs. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models by 0.09 and surpasses GPT-5 in a zero-shot setting by 0.08. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
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| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |
Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models (2026.acl-long)
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| Challenge: | Recent studies suggest that RLVR amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities. |
| Approach: | They propose a framework for RLVR that extends the spatial reasoning boundary . they use a mapping framework where the difficulty is precisely regulated by path length and number of turns . |
| Outcome: | The proposed framework extends the spatial reasoning boundary on two real-world navigation benchmarks. |
Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages (2026.findings-acl)
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| Challenge: | Speech deepfakes are highly realistic and can generate a few seconds of recorded speech. |
| Approach: | They propose an ALM that integrates semantic and prosodic representations from Whisper and TRILLsson to generate a speech deepfake dataset. |
| Outcome: | The proposed framework outperforms existing ALMs on the ICF benchmark in Indic languages. |