Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding | Paper Citation Record

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Unresolved cited work

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Many-Shot In-Context Learning

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding o l \"u c \

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding PaLM 2 Technical Report

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding What is the Role of Small Models in the LLM Era: A Survey

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Dinu, and Stefan Dumitrescu

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding The Llama 3 Herd of Models

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Eberhard, Gary F

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Do Multilingual Language Models Think Better in English?

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Gemma: Open Models Based on Gemini Research and Technology

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Mixtral of Experts

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Mahmudul Haque, Zakaria Masud Jiyad, M

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Crowdsourcing a Word-Emotion Association Lexicon

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding GPT-4 Technical Report

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Randolph

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding EmoBench: Evaluating the Emotional Intelligence of Large Language Models

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Evaluating the Zero-shot Robustness of Instruction-tuned Language Models

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Unresolved cited work

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding No Language Left Behind: Scaling Human-Centered Machine Translation

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya

Reference 65

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation

Reference 66

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 67

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Reference 68

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning

Reference 69

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 70

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Unresolved cited work

Reference 71

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Reference 72

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Unresolved cited work

Reference 73

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis

Reference 74

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Reference 75

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

Reference 76

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model

Reference 77

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding online" 'onlinestring :=

Reference 78

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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding write newline

Reference 79

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Outbound references

Pith citing papers