Papers with candidate selection
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are a powerful tool for creating synthetic replicas of private text. |
| Approach: | They propose a method for creating privacy-preserving synthetic data using private seeds and a formal differential privacy mechanism. |
| Outcome: | The proposed method achieves high fidelity to private data while providing strong privacy protection. |
Adversarial Training for Weakly Supervised Event Detection (N19-1)
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| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets. |
It’s All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models (2026.eacl-long)
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| Challenge: | Existing approaches to EL for historical texts require substantial training data or rely on domain-specific rules that limit scalability. |
| Approach: | They propose an unsupervised ensemble approach combining a Small Language Model and an LLM for historical EL. |
| Outcome: | The proposed approach outperforms state-of-the-art models on four established benchmarks in six European languages from the 19th and 20th centuries. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
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| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking (2024.findings-emnlp)
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| Challenge: | Multiple-choice cloze tests are a prevalent form of assessment that evaluates students' comprehension and inference abilities. |
| Approach: | They propose a framework for distractor generation using readily available pre-trained language models . human evaluations confirm that their approach produces more effective distractors . |
| Outcome: | The proposed framework outperforms existing methods without training or fine-tuning human evaluations confirm it. |
A Class-Rebalancing Self-Training Framework for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Distant supervision reduces the reliance on human annotation in named entity recognition tasks. |
| Approach: | They propose a class-rebalancing self-training framework for improving distantly-supervised named entity recognition by using a flexible threshold and a hybrid pseudo label. |
| Outcome: | The proposed model achieves state-of-the-art on five flat and two nested datasets and compares with other methods on the same dataset. |
FineRAG: Fine-grained Retrieval-Augmented Text-to-Image Generation (2025.coling-main)
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| Challenge: | Recent advances in text-to-image generation still exhibit limitations in terms of knowledge access. |
| Approach: | They propose a fine-grained retrieval-augmented image generation model that breaks down the retrieval task into four critical stages: query decomposition, candidate selection, retrieval augmented diffusion, and self-reflection. |
| Outcome: | The proposed method significantly reduces noise associated with retrieval-augmented image generation and performs better in complex, open-world scenarios. |
Open-World Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models do not evaluate their performance in academic research . authors aim to identify authors from anonymous text without additional information . |
| Approach: | They propose a benchmark to quantitatively assess LLMs' ability to infer author from text . they propose 'open-world' authorship attribute' to be a two-stage framework . |
| Outcome: | The proposed approach achieves 60.7% accuracy and 44.3% accuracy in two stages. |