TRACE: A Corpus of Team Creative Discussions (2026.acl-long)

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Challenge: Existing studies on team creativity lack the ability to observe discussion dynamics from the perspective of natural language processing (NLP) Standard approaches capture participants' perceptions rather than actual behavior.
Approach: They propose a corpus of 309 group discussions from 103 teams across six creative problem-solving tasks.
Outcome: The proposed analysis reveals that large teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content.

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Probing the “Creativity” of Large Language Models: Can models produce divergent semantic association? (2023.findings-emnlp)

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Challenge: Large language models possess remarkable capacity for processing language, but it remains unclear whether they can further generate creative content.
Approach: They utilize the divergent association task (DAT) to examine the creative thinking of large language models through a cognitive perspective.
Outcome: The proposed model outperforms the greedy search strategy while outperforming the average human level.
Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations (2026.eacl-long)

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Challenge: Creativity measures that distinguish creativity in one domain fail in others, and different metrics disagree on the same data points.
Approach: They examine, analyze, and compare four representative creativity measures across the diverse creative domains, including creative writing, unconventional problem-solving, and research ideation.
Outcome: The measures of creativity across creative domains are compared using a set of human-aligned examples and lack consistency across domains and metrics.
Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus (L18-1)

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Challenge: a corpus of human-computer interactions recorded in multiple modalities is being developed to study and model collaborative aspects of multimodal behavior in groups.
Approach: They propose to use a multimodal corpus to investigate collaborative aspects of multimodal behavior in groups that perform simple tasks.
Outcome: The proposed corpus is designed for public release and includes survey materials, personality tests and experience assessment questionnaires filled in by all participants.
Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models (2025.emnlp-main)

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Challenge: Recent studies evaluate the creative capabilities of large language models (LLMs) through diverse tasks, aiming to understand their strengths and limitations.
Approach: They propose to ask LLMs to generate Parallel Chains of Associations to Evaluate their creativity.
Outcome: The proposed framework minimizes the risk of data contamination and offers a highly efficient evaluation.
Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models (2026.findings-eacl)

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Challenge: Large language models are increasingly used in verbal creative tasks.
Approach: They propose a divergent association task that focuses on novelty, ignoring appropriateness, a core component of creativity.
Outcome: The proposed model scores are lower than baselines with no creative abilities, undermining its validity for model evaluation.
The Discussion Tracker Corpus of Collaborative Argumentation (2020.lrec-1)

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Challenge: The Discussion Tracker corpus is an annotated dataset of transcripts of spoken, multi-party argumentation transcribed from 985 minutes of audio .
Approach: They analyze 29 multi-party arguments transcribed from 985 minutes of audio . they provide descriptive statistics and code for predicting each dimension separately.
Outcome: The Discussion Tracker corpus was collected in high school English classes and annotated for argument moves, specificity, specificities and collaboration dimensions.
Automated Creativity Evaluation of Language Models Across Open-Ended Tasks (2026.acl-long)

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Challenge: Existing methods for evaluating creativity are tightly coupled to specific tasks and limiting scalability and generality.
Approach: They propose a domain-agnostic framework for quantifying LLM creativity across open-ended tasks.
Outcome: The proposed framework captures key facets of creativity including novelty, diversity, and task fulfilment with over 60% improved efficiency.
Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity (2025.emnlp-main)

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Challenge: Evaluating creativity is challenging, even for humans, because of its subjectivity and complex cognitive processes.
Approach: They propose a set of tasks to break down visual advertisement creativity into atypicality and originality with fine-grained annotations by humans.
Outcome: The proposed tasks demonstrate the promise and challenges of using VLMs for automated creativity assessment.
Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation (2026.findings-acl)

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Challenge: Large language models (LLMs) are increasingly used for creative tasks such as literary translation.
Approach: They propose a paired-task framework that assesses translational creativity using Units of Creative Potential (UCPs) they benchmark 23 models and four creativity-oriented prompts to assess translational comprehension .
Outcome: The proposed framework compares 23 models and four creativity-oriented prompts on literary excerpts from 11 books.
The RIP Corpus of Collaborative Hypothesis-Making (2024.lrec-main)

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Challenge: Existing studies on hypothesis generation and collaborative problem solving combine these two fields but there is still a gap between the two.
Approach: They propose to use a fictionalised murder investigation game as an environment to investigate how hypotheses are generated in group environments.
Outcome: The proposed corpus shows the emergent roles individuals took on and the strategies the groups employed, showing what can be gained through a deeper exploration of this domain.

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