# Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT

arXiv: https://arxiv.org/abs/2303.03186 · HTML: https://mlsanity.com/paper/2303.03186/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)

## Built on by (most cited first, 30 of 41)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions](https://mlsanity.com/paper/2406.05688/) (2024)
- [Large Language Models for Education: A Survey](https://mlsanity.com/paper/2405.13001/) (2024)
- [A Wide Evaluation of ChatGPT on Affective Computing Tasks](https://mlsanity.com/paper/2308.13911/) (2023)
- [Large Language Models are Complex Table Parsers](https://mlsanity.com/paper/2312.11521/) (2023)
- [Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health](https://mlsanity.com/paper/2304.10447/) (2023)
- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
- [Fine-grained Affective Processing Capabilities Emerging from Large Language Models](https://mlsanity.com/paper/2309.01664/) (2023)
- [Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs](https://mlsanity.com/paper/2402.11958/) (2024)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language Models](https://mlsanity.com/paper/2311.09189/) (2023)
- [Bias in Emotion Recognition with ChatGPT](https://mlsanity.com/paper/2310.11753/) (2023)
- [From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models](https://mlsanity.com/paper/2311.13063/) (2023)
- [Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects](https://mlsanity.com/paper/2312.04578/) (2023)
- [On Prompt Sensitivity of ChatGPT in Affective Computing](https://mlsanity.com/paper/2403.14006/) (2024)
- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses](https://mlsanity.com/paper/2409.15687/) (2024)
- [Can ChatGPT's Responses Boost Traditional Natural Language Processing?](https://mlsanity.com/paper/2307.04648/) (2023)
- [From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis](https://mlsanity.com/paper/2312.04720/) (2023)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding](https://mlsanity.com/paper/2510.04899/) (2025)
- [Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media](https://mlsanity.com/paper/2307.02313/) (2023)
- [APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT](https://mlsanity.com/paper/2402.01697/) (2024)

## Abstract (arXiv)

ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks. In this work, we evaluate the capabilities of ChatGPT to perform text classification on three affective computing problems, namely, big-five personality prediction, sentiment analysis, and suicide tendency detection. We utilise three baselines, a robust language model (RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), and a simple bag-of-words baseline (BoW). Results show that the RoBERTa trained for a specific downstream task generally has a superior performance. On the other hand, ChatGPT provides decent results, and is relatively comparable to the Word2Vec and BoW baselines. ChatGPT further shows robustness against noisy data, where Word2Vec models achieve worse results due to noise. Results indicate that ChatGPT is a good generalist model that is capable of achieving good results across various problems without any specialised training, however, it is not as good as a specialised model for a downstream task.