# AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models

arXiv: https://arxiv.org/abs/2409.18339 · HTML: https://mlsanity.com/paper/2409.18339/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting](https://mlsanity.com/paper/2309.08108/) (2023)
- [Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health](https://mlsanity.com/paper/2408.07313/) (2024)
- [From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs](https://mlsanity.com/paper/2408.17026/) (2024)
- [ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT](https://mlsanity.com/paper/1904.00132/) (2019)
- [Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition](https://mlsanity.com/paper/2012.05429/) (2020)
- [Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation](https://mlsanity.com/paper/2402.12862/) (2024)
- [NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep](https://mlsanity.com/paper/1904.03223/) (2019)
- [Distribution-based Emotion Recognition in Conversation](https://mlsanity.com/paper/2211.04834/) (2022)

## Built on by (most cited first, 9 of 9)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Beyond Scale: Small Language Models are Comparable to GPT-4 in Mental Health Understanding](https://mlsanity.com/paper/2507.08031/) (2025)
- [Emotions, Context, and Substance Use in Adolescents: A Large Language Model Analysis of Reddit Posts](https://mlsanity.com/paper/2501.14037/) (2025)
- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)
- [Scaling Ambiguity: Augmenting Human Annotation in Speech Emotion Recognition with Audio-Language Models](https://mlsanity.com/paper/2601.14620/) (2026)
- [Unraveling Emotions with Pre-Trained Models](https://mlsanity.com/paper/2510.19668/) (2025)
- [Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction](https://mlsanity.com/paper/2603.08230/) (2026)
- [Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition](https://mlsanity.com/paper/2607.14769/) (2026)

## Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent studies have shown LLMs' capability in recognizing emotions, but they often focus on single emotion labels and overlook the complex and ambiguous nature of human emotions. This study is the first to address this gap by exploring the potential of LLMs in recognizing ambiguous emotions, leveraging their strong generalization capabilities and in-context learning. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition. Experiments conducted using three datasets indicate significant potential for LLMs in recognizing ambiguous emotions, and highlight the substantial benefits of including context information. Furthermore, our findings indicate that LLMs demonstrate a high degree of effectiveness in recognizing less ambiguous emotions and exhibit potential for identifying more ambiguous emotions, paralleling human perceptual capabilities.