Papers with collaborative approach
The KIND Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection (2024.eacl-srw)
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Asma Yamani, Raghad Alziyady, Reem AlYami, Salma Albelali, Leina Albelali, Jawharah Almulhim, Amjad Alsulami, Motaz Alfarraj, Rabeah Al-Zaidy
| Challenge: | Nuanced dialects are a linguistic variant that pose several challenges for NLP models and techniques. |
| Approach: | They propose an approach to collect high quality Arabic dialect data by social collaboration . they use short texts to collect Arabic dialects and a KIND corpus . |
| Outcome: | The proposed approach is based on the KIND corpus of Arabic dialect data . it provides a high quality dataset and is versatile enough to be multipurpose . |
MEGAnno+: A Human-LLM Collaborative Annotation System (2024.eacl-demo)
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| Challenge: | Large language models (LLMs) can label data faster and cheaper than humans . however, they may fall short in understanding of complex contexts, leading to incorrect labels . |
| Approach: | They propose a collaborative approach where humans and LLMs work together to produce reliable labels. |
| Outcome: | The proposed system can produce reliable and high-quality labels faster and cheaper than humans . compared to traditional models, it can generate labels faster, at a lower cost . |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
| Outcome: | The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios. |
Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs (2024.acl-long)
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| Challenge: | Existing methods to determine the knowledge an LLM already possesses and the knowledge that requires the help of a search engine are expensive and require excessive computational costs. |
| Approach: | They propose a slim proxy model that detects missing knowledge in LLMs with a proxy model and use it to perform retrieval for the missing knowledge. |
| Outcome: | The proposed approach detects missing knowledge in LLMs with a slim proxy model and takes its answers as heuristic answers. |
ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods for visual commonsense reasoning (VCR) use pre-trained large language models and pre-training visionlanguage models. |
| Approach: | They propose a collaborative approach where pre-trained LLMs serve as problem classifiers to analyze problem category and either use VLMs to answer directly or actively instruct LLM to gather relevant visual elements to support potential commonsense inferences. |
| Outcome: | The proposed approach outperforms all other methods without in-domain fine-tuning on two VCR benchmark datasets. |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Causal Graph based Event Reasoning using Semantic Relation Experts (2025.acl-long)
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| Challenge: | Recent advances in event reasoning have limited ability to accurately identify causal connections between events. |
| Approach: | They propose a collaborative approach to generate correct graphs and graphs to assist reasoning . they propose 'a causal chain of events' task that requires a causal link between events . |
| Outcome: | The proposed approach achieves competitive results with state-of-the-art models on forecasting and next event prediction tasks. |