Papers with zero-shot baseline
Analysis and Prediction of NLP Models via Task Embeddings (2022.lrec-1)
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| Challenge: | Pretrained transformer-based encoders can be used to acquire rich text representations but need additional task supervision to be useful for downstream tasks. |
| Approach: | They propose a transformer to each MetaEval task and a neural network with a weighted encoder to perform the embeddings. |
| Outcome: | The proposed model outperforms baselines on GLUE tasks and can be used as a benchmark for future transfer learning research. |
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) like GPT-4 are not able to handle multi-modal open-domain question answering in a zero-shot manner. |
| Approach: | MoqaGPT uses divide-and-conquer strategy to extract answers from each modality separately. |
| Outcome: | MoqaGPT improves on MMCoQA dataset by +37.91 points and EM by +34.07 points. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
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| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |
Code4Struct: Code Generation for Few-Shot Event Structure Prediction (2023.acl-long)
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| Challenge: | Large Language Models (LLMs) trained on a mixture of text and code have demonstrated impressive capability in translating natural language (NL) into structured code. |
| Approach: | They propose to use programming language (PL) inheritance and type annotations to translate text into code to tackle structured prediction tasks. |
| Outcome: | The proposed model outperforms existing models on 20-shot data by 29.5% absolute F1. |
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs (2021.eacl-main)
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| Challenge: | Existing methods for few-shot link prediction are limited by having only a few examples of a relation . low-frequency relations are abundant in knowledge graphs, but link prediction for these relations is important . |
| Approach: | They perform few-shot link prediction for a set of new relations unseen during training, given only a few examples of each relation at test time. |
| Outcome: | The proposed model is based on a simple, zero-shot baseline that ignores relation-specific information and achieves surprisingly strong performance. |
Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization (2026.eacl-long)
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| Challenge: | Text2Vis systems generate functional code but resulting charts lack semantic alignment and clarity. |
| Approach: | They propose a framework that integrates post-execution feedback with textual accuracy, code validity, and visualization quality. |
| Outcome: | The proposed framework outperforms strong zero-shot and supervised baselines and shows robust generalization to out-of-domain datasets. |
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2022.acl-long)
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| Challenge: | Visual referring expression comprehension (ReC) models can be trained for a domain, but it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC. |
| Approach: | They propose a method that repurposes CLIP, a state-of-the-art large-scale model, for training a referring expression comprehension model for a new visual domain. |
| Outcome: | The proposed model reduces the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on ReFGTA (video game imagery), and its relative improvement over supervised ReC models is 8%. |
DocMMIR: A Framework for Document Multi-modal Information Retrieval (2025.findings-emnlp)
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| Challenge: | Existing multi-modal information retrieval models lack a comprehensive exploration of document-level retrieval . existing models suffer from the absence of cross-domain datasets at this granularity. |
| Approach: | They propose a multi-modal document retrieval framework to unify diverse document formats and domains with a comprehensive retrieval scenario. |
| Outcome: | The proposed framework improves document retrieval performance on a large multimodal dataset. |
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model (2024.findings-emnlp)
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| Challenge: | Existing studies show that training examples improve zero-shot performance of supervised ranking models. |
| Approach: | They propose to augment supervised ranking models with pairs of queries and documents to improve their performance. |
| Outcome: | The proposed model outperforms the unsupervised models on in-domain and out-domain retrieval benchmarks. |
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World (2025.findings-acl)
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Sina Semnani, Pingyue Zhang, Wanyue Zhai, Haozhuo Li, Ryan Beauchamp, Trey Billing, Katayoun Kishi, Manling Li, Monica Lam
| Challenge: | Using a partially reannotated subset of the Armed Conflict Location & Event Data, we analyze 39,786 conflict events across 20 languages and 171 countries. |
| Approach: | They propose a large-scale conflict event dataset with extensive coverage of region-specific entities. |
| Outcome: | The proposed method detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. |
SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models (2025.emnlp-main)
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| Challenge: | Visual Document Retrieval (VDR) relies on text-to-image retrieval using specialized bi-encoders . et al., 2022, 2024, 2021, 2023, 2026, 2030, 2040, 2050, 2060) document retrieval bridges human or artificial agents to the most relevant information, authors say . |
| Approach: | They propose a zero-shot visual document retrieval method that uses bi-encoders . they propose 63.4% nDCG@5 for visual document capture and a reusable semantic proxy . |
| Outcome: | The proposed method surpasses the strongest specialised multi-vector visual document encoder on the ViDoRe-v2 benchmark and scales similarly on MIRACL-VISION with broader multilingual coverage. |