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ResearchFormerly WizWand

Target-Aspect-Sentiment Detection on Coursera

52.94F1 ScoreLLM
19.857628.446337.03545.6237Mar 2, 2026Mar 16, 2026Mar 30, 2026Apr 14, 2026Apr 28, 2026May 12, 2026May 27, 2026
Updated 5mo ago

Evaluation results

MethodLinks
LLMMode=Fine-tuned2026.0552.94
Para.Mode=Fine-tuned2026.0551.86
MvPMode=Fine-tuned2026.0550.97
LLM-MvP# Shots=1002026.0549.12
LLM-MvP# Shots=502026.0547.22
Self-consistency# Shots=1002026.0546.83
Single-order# Shots=1002026.0546.09
Self-consistency# Shots=502026.0544.9
Single-order# Shots=502026.0544.06
Gemma-3-27B ICLScenario=10 annotated examples, # Train=102026.0341.69
LLM-MvP# Shots=102026.0541.01
Reasoning# Shots=1002026.0539.96
LLM-MvP# Shots=Avg.2026.0539.79
Self-consistency# Shots=102026.0539.44
LA-ABSA w/ DLOScenario=10 annotated examples, # Train=full2026.0339.22
Single-order# Shots=102026.0538.52
Self-consistency# Shots=Avg.2026.0538.49
LA-ABSA w/ ParaphraseScenario=10 annotated examples, # Train=full2026.0338.46
Single-order# Shots=Avg.2026.0537.45
Reasoning# Shots=502026.0536.92
Reasoning# Shots=Avg.2026.0534.16
Reasoning# Shots=102026.0533.5
LA-ABSA w/ ParaphraseScenario=0 annotated examples, # Train=full2026.0330.92
LA-ABSA w/ DLOScenario=0 annotated examples, # Train=full2026.0330.6
Gemma-3-27B (0-shot)Scenario=0 annotated examples, # Train=02026.0329.5
Reasoning# Shots=02026.0526.26
Self-consistency# Shots=02026.0522.8
LLM-MvP# Shots=02026.0521.81
Single-order# Shots=02026.0521.13