Pittawat Taveekitworachai — accepted AI-conference papers

2025

Prior Prompt Engineering for Reinforcement Fine-Tuning

Pittawat Taveekitworachai, Potsawee Manakul, Sarana Nutanong, Kunat Pipatanakul

EMNLP 2025

This paper investigates prior prompt engineering (pPE) in the context of reinforcement fine-tuning (RFT), where language models (LMs) are incentivized to exhibit behaviors that maximize performance through reward signals. While existing RFT research has primarily focused on algorithms, reward shapin

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2024

Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination

Pittawat Taveekitworachai, Febri Abdullah, Ruck Thawonmas

EMNLP 2024main

This paper presents a series of investigations into an interesting phenomenon where we observe performance increases in large language models (LLMs) when providing a prompt that causes and exploits hallucination. We propose null-shot prompting, a counter-intuitive approach where we intentionally ins…

Cited by 3 Source ↗PDF ↗Scholar ↗