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Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large
Language Models

v1v2 (latest)

Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models

17 June 2024

Scott Barnett

Zac Brannelly

Stefanus Kurniawan

ArXiv (abs)PDF HTML

Papers citing "Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models"

3 / 3 papers shown

In-Context Distillation with Self-Consistency Cascades: A Simple, Training-Free Way to Reduce LLM Agent Costs

In-Context Distillation with Self-Consistency Cascades: A Simple, Training-Free Way to Reduce LLM Agent Costs

Vishnu Sarukkai

Kayvon Fatahalian

73

0

0

02 Dec 2025

From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding

From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding

Farjana Sultana Mim

Kristen Wendell

116

2

0

25 Nov 2025

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?

Vishnu Suresh Lokhande

165

0

0

25 Jul 2025

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