# The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth arXiv: https://arxiv.org/abs/2402.11886 ยท HTML: https://mlsanity.com/paper/2402.11886/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 87 references that are in the map. - First posted on arXiv 19 Feb 2024. ## Builds on (most cited first, 18 of 18) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022) - [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023) - [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020) - [Orca 2: Teaching Small Language Models How to Reason](https://mlsanity.com/paper/2311.11045/) (2023) - [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021) - [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021) - [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017) - [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023) - [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022) - [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022) - [Modeling Naive Psychology of Characters in Simple Commonsense Stories](https://mlsanity.com/paper/1805.06533/) (2018) - [A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training](https://mlsanity.com/paper/2305.02031/) (2023) - [Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling](https://mlsanity.com/paper/2311.09243/) (2023) ## Built on by (most cited first, 20 of 20) - [The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs](https://mlsanity.com/paper/2501.10970/) (2025) - [NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://mlsanity.com/paper/2404.12464/) (2024) - [On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs](https://mlsanity.com/paper/2407.19200/) (2024) - [Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance](https://mlsanity.com/paper/2410.18889/) (2024) - [Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy](https://mlsanity.com/paper/2510.10328/) (2025) - [A Comprehensive Evaluation of Large Language Models on Mental Illnesses](https://mlsanity.com/paper/2409.15687/) (2024) - [Empathy Through Multimodality in Conversational Interfaces](https://mlsanity.com/paper/2405.04777/) (2024) - [COMET: "Cone of experience" enhanced large multimodal model for mathematical problem generation](https://mlsanity.com/paper/2407.11315/) (2024) - [From Rational Answers to Emotional Resonance: The Role of Controllable Emotion Generation in Language Models](https://mlsanity.com/paper/2502.04075/) (2025) - [NL-Eye: Abductive NLI for Images](https://mlsanity.com/paper/2410.02613/) (2024) - [Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity](https://mlsanity.com/paper/2508.07902/) (2025) - [LLMs Reproduce Stereotypes of Sexual and Gender Minorities](https://mlsanity.com/paper/2501.05926/) (2025) - [AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries](https://mlsanity.com/paper/2508.11285/) (2025) - [Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models](https://mlsanity.com/paper/2505.19621/) (2025) - [Multi-Domain Explainability of Preferences](https://mlsanity.com/paper/2505.20088/) (2025) - [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026) - [Exploring LGBTQ+ Bias in Generative AI Answers across Different Country and Religious Contexts](https://mlsanity.com/paper/2407.03473/) (2024) - [Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layers](https://mlsanity.com/paper/2604.20166/) (2026) - [Understanding helpfulness and harmless tension in reward models](https://mlsanity.com/paper/2606.13209/) (2026) - [Evaluating Cultural Awareness of LLMs for Haitian Creole](https://mlsanity.com/paper/2609.31506/) (2026) ## Abstract (arXiv) Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation. Hindered by negative stigma, they often avoid seeking help and rely on online resources, which may provide incompatible information. Although access to a supportive environment and reliable information is invaluable, many queer youth worldwide have no access to such support. However, this could soon change due to the rapid adoption of Large Language Models (LLMs) such as ChatGPT. This paper aims to comprehensively explore the potential of LLMs to revolutionize emotional support for queers. To this end, we conduct a qualitative and quantitative analysis of LLM's interactions with queer-related content. To evaluate response quality, we develop a novel ten-question scale that is inspired by psychological standards and expert input. We apply this scale to score several LLMs and human comments to posts where queer youth seek advice and share experiences. We find that LLM responses are supportive and inclusive, outscoring humans. However, they tend to be generic, not empathetic enough, and lack personalization, resulting in nonreliable and potentially harmful advice. We discuss these challenges, demonstrate that a dedicated prompt can improve the performance, and propose a blueprint of an LLM-supporter that actively (but sensitively) seeks user context to provide personalized, empathetic, and reliable responses. Our annotated dataset is available for further research.