mlsanityData to 7 Oct 2026

Sentiment Reasoning for Healthcare

arXiv 2407.21054 ↗First posted 24 Jul 2024cs.CL cs.AI cs.LGcited by 0

On the map: LLM reasoning → Audio-language model reasoning

Abstract (arXiv)

Transparency in AI healthcare decision-making is crucial. By incorporating rationales to explain reason for each predicted label, users could understand Large Language Models (LLMs)'s reasoning to make better decision. In this work, we introduce a new task - Sentiment Reasoning - for both speech and text modalities, and our proposed multimodal multitask framework and the world's largest multimodal sentiment analysis dataset. Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. Our study conducted on both human transcripts and Automatic Speech Recognition (ASR) transcripts shows that Sentiment Reasoning helps improve model transparency by providing rationale for model prediction with quality semantically comparable to humans while also improving model's classification performance (+2% increase in both accuracy and macro-F1) via rationale-augmented fine-tuning. Also, no significant difference in the semantic quality of generated rationales between human and ASR transcripts. All code, data (five languages - Vietnamese, English, Chinese, German, and French) and models are published online: https://github.com/leduckhai/Sentiment-Reasoning

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning. Builds on 25 of its 117 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

25 of 117 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  6. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  7. 2023Mistral 7Bcited by 3,065
  8. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  9. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  10. 2024Qwen2-Audio Technical Reportcited by 543
  11. 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 335
  12. 2022Large Language Models Are Reasoning Teacherscited by 318
13 more
  1. 2016MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videoscited by 193
  2. 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176
  3. 2020PhoBERT: Pre-trained language models for Vietnamesecited by 148
  4. 2022ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generationcited by 37
  5. 2021BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamesecited by 33
  6. 2022Crosslingual Generalization through Multitask Finetuningcited by 26
  7. 2020Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odysseycited by 21
  8. 2022Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysiscited by 16
  9. 2024Investigating Mysteries of CoT-Augmented Distillationcited by 15
  10. 2024Distilling Reasoning Ability from Large Language Models with Adaptive Thinkingcited by 13
  11. 2019wav2vec: Unsupervised Pre-training for Speech Recognitioncited by 7
  12. 2021UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Datasetcited by 7
  13. 2022Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errorscited by 7

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  1. 2026Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis
  2. 2025Reasoning Beyond Labels: Measuring LLM Sentiment in Low-Resource, Culturally Nuanced Contexts
  3. 2026ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
  4. 2025Multimodal Large Language Models Meet Multimodal Emotion Recognition and Reasoning: A Survey
  5. 2026From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning
  6. 2024Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
  7. 2025Reason2Decide: Rationale-Driven Multi-Task Learning
  8. 2025Systematic Evaluation of Machine-Generated Reasoning and PHQ-9 Labeling for Depression Detection Using Large Language Models