Customising General Large Language Models for Specialised Emotion Recognition Tasks

• URL: http://arxiv.org/abs/2310.14225v1
• Date: Sun, 22 Oct 2023 08:09:13 GMT
• Title: Customising General Large Language Models for Specialised Emotion Recognition Tasks
• Authors: Liyizhe Peng, Zixing Zhang, Tao Pang, Jing Han, Huan Zhao, Hao Chen, Bj\"orn W. Schuller
• Abstract summary: We investigate how large language models (LLMs) perform in linguistic emotion recognition. Specifically, we exemplify a publicly available and widely used LLM -- Chat General Language Model. We customise it for our target by using two different modal adaptation techniques, i.e., deep prompt tuning and low-rank adaptation. The experimental results show that the adapted LLM can easily outperform other state-of-the-art but specialised deep models.
• Score: 24.822342337306363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy, universality, explanation, robustness, few/zero-shot learning, and others. Leveraging the capability of LLMs inevitably becomes an essential solution for emotion recognition. To this end, we further comprehensively investigate how LLMs perform in linguistic emotion recognition if we concentrate on this specific task. Specifically, we exemplify a publicly available and widely used LLM -- Chat General Language Model, and customise it for our target by using two different modal adaptation techniques, i.e., deep prompt tuning and low-rank adaptation. The experimental results obtained on six widely used datasets present that the adapted LLM can easily outperform other state-of-the-art but specialised deep models. This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition.

Related papers

• Retrieving Implicit and Explicit Emotional Events Using Large Language Models [4.245183693179267]
  Large language models (LLMs) have garnered significant attention in recent years due to their impressive performance. This study investigates LLMs' emotion retrieval capabilities in commonsense.
  arXiv  Detail & Related papers  (2024-10-24T19:56:28Z)
• Do Large Language Models Possess Sensitive to Sentiment? [18.88126980975737]
  Large Language Models (LLMs) have recently displayed their extraordinary capabilities in language understanding. This paper investigates the ability of LLMs to detect and react to sentiment in text modal.
  arXiv  Detail & Related papers  (2024-09-04T01:40:20Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
  Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs. Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
  arXiv  Detail & Related papers  (2024-02-26T09:36:05Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations [28.15933355881604]
  Large language models (LLMs) have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks. We propose DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning LLaMA models. We offer a comprehensive evaluation of our proposed model on three benchmarking emotion recognition in conversations datasets.
  arXiv  Detail & Related papers  (2023-10-17T16:15:34Z)
• Large Language Models Are Not Strong Abstract Reasoners [12.354660792999269]
  Large Language Models have shown tremendous performance on a variety of natural language processing tasks. It is unclear whether LLMs can achieve human-like cognitive capabilities or whether these models are still fundamentally circumscribed. We introduce a new benchmark for evaluating language models beyond memorization on abstract reasoning tasks.
  arXiv  Detail & Related papers  (2023-05-31T04:50:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.