📢 The Three-hop (💡aspect + 🤔opinion + 🧠reason) Chain-of-Thought concept + LLM represent a decent concept for reasoning emotions of participants in textual dialogues.
Delighted to share the tutorial video which make you aware of:
✅ The proper application of LLM towards implicit IR
✅ Ways for aligning different information types (causes and states) within the same LLM
✅ Launch your LLM in GoogleColab that is capable for characters Emotion Extraction in dialogues 🧪

🎥: https://www.youtube.com/watch?v=vRVDQa7vfkU

Project: https://github.com/nicolay-r/THOR-ECAC
Paper: https://aclanthology.org/2024.semeval-1.4/
Model card:
nicolay-r/flan-t5-emotion-cause-thor-base

YouTube

How to Extract Emotions using Large Language Models?

This video shares the reasoning concepts adaption forretrieving emotions of dialogue characters via Large Language Models (LLM). We fine-tune Flan-T5-base (250M) with the reforged THoR-ISA framework for Emotion Cause Extraction. Our model took 3'rd place…