Challenge: Recent studies have shown that user-level features can carry more task-related information than the text itself.
Approach: They evaluate multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users’ self-expression and psychological attributes.
Outcome: The proposed models improve on the language and traits of users, while lacking rich annotations of other attributes.

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Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models (2023.findings-emnlp)

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Challenge: Existing work on automatic prediction of cognitive appraisals has focused on physiological aspects of emotions.
Approach: They present a dataset that assesses 24 appraisal dimensions across 241 Reddit posts . they find that open-source models fail to automatically assess and explain cognitive appraisals .
Outcome: The proposed dataset assesses 24 appraisal dimensions across 241 reddit posts.
Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)

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Challenge: a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance .
Approach: They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance .
Outcome: The proposed model can quantify subjectivity in cognitive appraisal using fine-tuned models and prompt-based large language models.
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)

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Challenge: Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications.
Approach: They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality.
Outcome: The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services.
Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers (2021.findings-emnlp)

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Challenge: Large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, but statistical bias in benchmark data and probing studies has recently called into question their true capabilities.
Approach: They propose to evaluate systems through a measure of prediction coherence by using two existing language understanding benchmarks with different properties to demonstrate its versatility.
Outcome: The proposed evaluation framework is quick, effective, and versatile to provide insight into the coherence of machines’ predictions.
Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)

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Challenge: a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability.
Approach: They draw on a psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next.
Outcome: The proposed models do not resemble human language users, the authors show . their models capture the linguistic knowledge required to perform discourse modeling .
The PEACE-Reviews dataset: Modeling Cognitive Appraisals in Emotion Text Analysis (2023.findings-emnlp)

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Challenge: Recent studies have delved into its significance, yet the interplay between various forms of cognitive appraisal and specific emotions, such as joy and anger, remains an area of exploration in consumption contexts.
Approach: They propose to construct a dataset to model the evaluations people make about their situations based on annotated autobiographical accounts of their emotional and appraisal experiences .
Outcome: The proposed model incorporates emotion, cognition, individual traits, and demographic data.
User Behavior Prediction as a Generic, Robust, Scalable, and Low-Cost Evaluation Strategy for Estimating Generalization in LLMs (2025.findings-acl)

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Challenge: We argue that knowledge-retrieval and reasoning tasks are not ideal for measuring generalization, as LLMs are not trained for specific tasks.
Approach: They propose a statistically motivated framework using personalization to assess generalization in Large Language Models.
Outcome: The proposed framework outperforms existing models on movie and music recommendation datasets, but all models have room for improvement, especially Llama.
Task and Sentiment Adaptation for Appraisal Tagging (2023.eacl-main)

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Challenge: Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions.
Approach: They propose to use language models to automatically identify and annotate text segments for appraisal.
Outcome: The proposed model achieves superior performance than baseline adapter-based models and other neural classification models for cross-domain and cross-language settings.
Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention (2020.acl-main)

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Challenge: Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative.
Approach: They propose a model that uses message-level attention to learn the relative weight of users’ social media posts for assessing their five factor personality traits.
Outcome: The proposed model outperforms models with word-level attention and yields state-of-the-art accuracies for all five personality traits.
Appraisal Theories for Emotion Classification in Text (2020.coling-main)

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Challenge: Automatic emotion categorization is based on textual units assigned to an emotion from a predefined inventory, for instance following the basic emotion classes proposed by Paul Ekman (1999) or Plutchik (2001).
Approach: They propose to make automatic emotion categorization explicit by following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models.
Outcome: The proposed models improve the classification of discrete emotion categories by using appraisal dimension assignments in event descriptions.

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