Papers with CD
PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation (2026.eacl-demo)
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Maged S. Al-shaibani, Zaid Alyafeai, Dania Refai, Nawaf Alomari, Ahmed Ashraf, Mais Alheraki, Mustafa Alturki, Hamzah Luqman, Irfan Ahmad
| Challenge: | PromptLab is a web-based prompt engineering platform for collaborative prompt development across diverse natural language processing tasks and datasets. |
| Approach: | They propose to integrate prompt generation via OpenRouter and provide real-time validation with multiple Large Language Models. |
| Outcome: | The platform addresses primary challenges in prompt development, including template creation, collaborative review, and quality assurance through a comprehensive workflow that supports both individual researchers and team-based projects. |
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. |
| Approach: | They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER . |
| Outcome: | The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages. |
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting (2025.naacl-industry)
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Emmanuel Aboah Boateng, Cassiano O Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Soundararajan Srinivasan, Victor Dibia
| Challenge: | Concept Distillation (CD) is an automated prompt optimization technique for enhancing weaker models on complex tasks. |
| Approach: | They propose an automatic prompt optimization technique for enhancing weaker models on complex tasks using a base prompt and a strong model to generate reasons for these mistakes. |
| Outcome: | The proposed technique improves weaker models on NL2Code and mathematical reasoning tasks, while preserving performance. |
Enriching children’s stories with LLMs: Delivering multilingual data enrichment for children’s books at scale and across markets (2025.acl-industry)
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| Challenge: | Using large language models and human-in-the-loop quality control, we enrich multilingual e-book and audio book data and make our product catalog easier to navigate for children. |
| Approach: | They propose a user-centered, empirically guided approach to multilingual metadata enrichment for children’s books using large language models and human-in-the-loop quality control. |
| Outcome: | The proposed approach delivers high-quality labels and improves user experience in real-world production environments. |
Contrastive Decoding Reduces Hallucinations in Large Multilingual Machine Translation Models (2024.eacl-long)
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| Challenge: | Hallucinations occur when the target side sentence is detached from the source side sentence, or in other words, when there is a low contribution of the source sentence to the generation of the target sentence. |
| Approach: | They propose to use Contrastive Decoding to maximise the log-likelihood difference between a model and the same model with reduced contribution from the encoder outputs. |
| Outcome: | The proposed algorithm maximises the log-likelihood difference between a model and the same model with reduced contribution from the encoder outputs. |
Revisiting Robust Neural Machine Translation: A Transformer Case Study (2021.findings-emnlp)
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| Challenge: | Recent advances in NMT have shown promising results but are vulnerable to noise. |
| Approach: | They propose a data-driven technique called Target Augmented Fine-tuning to incorporate noise during training. |
| Outcome: | The proposed techniques perform with no degradation where up to 10% of entire test words are infected by noise. |
Vision-aided Unsupervised Constituency Parsing with Multi-MLLM Debating (2025.findings-acl)
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| Challenge: | Existing approaches require explicit cross-modal alignment, but new approaches address these challenges. |
| Approach: | They propose a framework for vision-aided unsupervised constituency parsing . they leverage multimodal large language models pre-trained on diverse image-text or video-text data . |
| Outcome: | The proposed framework achieves state-of-the-art performance on image-text and video-text datasets, improving robustness and accuracy. |
Cross-document Coreference Resolution over Predicted Mentions (2021.findings-acl)
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| Challenge: | Cross-document coreference resolution has been under-explored in recent years . however, the challenge of cross-document resolution remains relatively under-studied . |
| Approach: | They propose a model for cross-document coreference resolution from raw text that extends a prominent withindocument corefer model to the CD setting. |
| Outcome: | The proposed model achieves competitive results for event and entity coreference resolution on gold mentions. |
Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM (2024.emnlp-main)
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| Challenge: | Contrastive decoding (CD) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. |
| Approach: | They propose a new unsupervised decoding method called Asymptotic Probability Decoding (APD) that extrapolates the probability curves from the LMs of different sizes to infer the asymptototic probabilities from an infinitely large LM. |
| Outcome: | The proposed method improves the next-token distribution of a large expert language model using a small amateur LM. |
Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis (2022.emnlp-main)
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| Challenge: | Existing approaches to speaker diarization treat speaker dependency and overlaps as multi-label classification problems. |
| Approach: | They propose to reformulate overlapped speaker diarization task as a single-label prediction problem via power set encoding (PSE) to overcome the disadvantages, they propose a speaker overlap-aware neural diarisation model which incorporates a context-independent scorer and a contextual-dependent score. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on speaker voice activity detection and improves relative diarization error reduction by 6.30%. |
Contrastive Decoding: Open-ended Text Generation as Optimization (2023.acl-long)
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Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis
| Challenge: | Using a language model, maximum probability is a poor decoding objective because it produces short and repetitive text. |
| Approach: | They propose a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. |
| Outcome: | The proposed approach outperforms four strong decoding algorithms in automatic and human evaluations across wikipedia, news and story domains. |
Consultant Decoding: Yet Another Synergistic Mechanism (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have attracted widespread attention and adoption across diverse domains due to their exceptional performance and robust generalization abilities. |
| Approach: | They propose a synergetic mechanism for Consultant Decoding (CD) that achieves a 2.5-fold increase in inference speed compared to the target model while maintaining comparable generation quality. |
| Outcome: | The proposed mechanism achieves 2.5-fold increase in inference speed while maintaining comparable generation quality (100% of the target model’s performance). |
A Survey of Cognitive Distortion Detection and Classification in NLP (2025.findings-emnlp)
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| Challenge: | despite momentum in natural language processing, the field remains fragmented . inconsistencies in CD taxonomies, task formulations and evaluation practices limit comparability . |
| Approach: | This review provides a comprehensive review of 38 studies spanning two decades . they map how CDs have been implemented in computational research and evaluate the methods applied. |
| Outcome: | The paper presents the first comprehensive review of 38 studies spanning two decades . it summarises common task setups and highlights persistent challenges to support more coherent research. |
UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding (2026.findings-acl)
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| Challenge: | Consistency models (CMs) have shown promise in the efficient generation of both image and text. |
| Approach: | They propose to use a discrete token for both image and text generation to achieve a unified denoising perspective. |
| Outcome: | The proposed model outperforms SD3 on GenEval and Image Reward while being 1.5 faster at long-sequence generating speed. |
Uncertainty-Aware Contrastive Decoding (2025.findings-acl)
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| Challenge: | Large language models excel in a wide range of tasks, but generating factually accurate outputs remains a challenge. |
| Approach: | They propose a method that dynamically adjusts model contributions at each decoding step based on uncertainty. |
| Outcome: | The proposed method significantly improves factual accuracy and reliability over existing methods. |
How to Set the Learning Rate for Large-Scale Pre-training? (2026.findings-acl)
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| Challenge: | Optimal configuration of the learning rate (LR) is a fundamental yet formidable challenge in large-scale pre-training. |
| Approach: | They propose a Fitting Paradigm and a Transfer Paradigme to investigate fit and transfer . they propose scalability and elucidate the reasons why module-wise parameter tuning underperforms . |
| Outcome: | The proposed model reduces the search complexity by reducing the search cost by lowering the search factor. |