Zizhou Liu, Ziwei Gong, Lin Ai, Zheng Hui, Run Chen, Colin Wayne Leach, Michelle R. Greene, Julia Hirschberg
| Challenge: | a holistic review systematically integrating psychology across the LLM lifecycle remains missing. |
| Approach: | They examine how psychological theories can inform stages of LLM development . they highlight current trends and gaps in how psychological theory is applied . |
| Outcome: | The authors highlight current trends and gaps in how psychological theories are applied . they argue that psychological insights have shaped pivotal NLP breakthroughs . |
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A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025.findings-acl)
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Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen
| Challenge: | Large language models (LLMs) can handle extensive context and multi-turn reasoning. |
| Approach: | They propose a taxonomy dividing psychotherapy into stages of assessment, diagnosis, and treatment to examine LLM advancements and challenges. |
| Outcome: | The proposed taxonomy reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. |
Large Human Language Models: A Need and the Challenges (2024.naacl-long)
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| Challenge: | a growing recognition of the importance of modeling human and social factors into human-centered NLP models . authors advocate for three positions toward creating large human language models based on psychological and behavioral sciences . |
| Approach: | et al. advocate for three positions toward creating large human language models . they argue that LM training should include the human context and recognize that people are more than their group . |
| Outcome: | a new study shows that learning language from linguistic signals alone is not adequate, according to a recent paper . authors advocate for three positions toward creating large human language models . a human-centered model should include the human context, and account for the dynamic nature of the human environment, they say . |
Social Intelligence in the Age of LLMs (2025.naacl-tutorial)
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| Challenge: | Large Language Models (LLMs) are a powerful tool for integrating human-like communication and context-aware interactions into artificial systems. |
| Approach: | They propose to introduce and overview different aspects of artificial social intelligence and their relationship with LLMs by introducing scientific methods for evaluating social intelligence in LLM. |
| Outcome: | This tutorial will introduce scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. |
Modeling, Evaluating, and Embodying Personality in LLMs: A Survey (2025.findings-emnlp)
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Iago Alves Brito, Julia Soares Dollis, Fernanda Bufon Färber, Pedro Schindler Freire Brasil Ribeiro, Rafael Teixeira Sousa, Arlindo Rodrigues Galvão Filho
| Challenge: | This survey provides a comprehensive overview of the LLM-driven personality scenario. |
| Approach: | This survey provides a comprehensive overview of the LLM-driven personality scenario. |
| Outcome: | The proposed taxonomy analyzes the limitations of existing methods and identifies key research gaps. |
Cognitive Effects and Biases in Large Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Approach: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Outcome: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
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| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
| Outcome: | The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks . |
Theory of Mind in Large Language Models: Assessment and Enhancement (2025.acl-long)
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| Challenge: | Theory of Mind (ToM) is a cornerstone of human social intelligence . Large Language Models (LLMs) are increasingly integrated into daily life . |
| Approach: | They analyze evaluation benchmarks and enhancement strategies to evaluate LLMs' ToM capabilities. |
| Outcome: | The proposed and widely used story-based benchmarks and enhancement strategies are used to evaluate LLMs' ToM capabilities. |
What Matters to an LLM? Behavioral and Computational Evidences from Summarization (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly entrusted with the management of information. |
| Approach: | They combine behavioral and computational analyses to find out what LLMs prioritize . they generate length-controlled summaries and derive empirical importance distributions . |
| Outcome: | The proposed model converges on consistent importance patterns and clusters more by family than by size. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |