| Challenge: | Recent work shows that an individual's worldview -or beliefs about the overall character of the world -can explain persistent behavioral patterns and correlates with personality, well-being, political, religious, and demographic variables. |
| Approach: | They develop a dataset of public opinion survey data from 858 US residents with written explanations from the respondents for why they hold specific opinions and the Primal World Belief survey for assessing respondent worldview. |
| Outcome: | The proposed model can be used to better represent an individual's belief system and improve opinion prediction. |
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| Challenge: | Personality is a defining feature of human beings, shaped by a complex interplay of demographic characteristics, moral principles, and social experiences. |
| Approach: | They use public opinion surveys to model past user opinions in addition to user demographics and ideology to achieve up to 7 points accuracy gains in predicting public opinions from survey questions. |
| Outcome: | The proposed model achieves 7 points accuracy gains in predicting public opinions from public opinion surveys across a broad set of topics. |
A Graph per Persona: Reasoning about Subjective Natural Language Descriptions (2024.findings-acl)
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| Challenge: | Existing large language models (LLMs) perform poorly in reasoning about subjective knowledge, showing strong biases and lack interpretability requirements. |
| Approach: | They propose a novel approach for reasoning about subjective knowledge that integrates potential and implicit meanings and explicitly models the relational nature of the information. |
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On the Interaction of Belief Bias and Explanations (2021.findings-acl)
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| Challenge: | Existing methods to evaluate explainability fail to account for belief biases affecting human performance . previous studies have shown that neural models can make confident predictions relying on artifacts . |
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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
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CLIX: Cross-Lingual Explanations of Idiomatic Expressions (2025.findings-acl)
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| Challenge: | Existing definition generation systems are difficult to use in second language learning due to the presence of unfamiliar words and grammar. |
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Aligning Large Language Models with Human Opinions through Persona Selection and Value–Belief–Norm Reasoning (2025.coling-main)
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| Challenge: | Current methods for reasoning and predicting human opinions employ role-playing with personae but face two major issues: LLMs are sensitive to even a single irrelevant persona, skewing predictions by up to 30%; and LLM fail to reason strategically over personas. |
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A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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| Challenge: | Symbolic meaning representations of natural language text have been studied since at least the 1960s . with the availability of large annotated corpora, the field has recently seen several new developments . |
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Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)
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| Challenge: | Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games. |
| Approach: | They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation . |
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From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models (2023.emnlp-main)
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| Challenge: | Existing large-scale surveys soliciting opinions on issues can be costly and laborious. |
| Approach: | They propose to use value-injected large language models to inject a target value distribution into large language model (LLM) and have them predict opinions and behaviors of people with similar values. |
| Outcome: | The proposed method significantly outperforms baseline methods on four tasks and the results suggest opinions and behaviors can be better predicted using value-injected LLMs. |