Papers by Pittawat Taveekitworachai
Prior Prompt Engineering for Reinforcement Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing studies have focused on algorithms, reward shaping, and data curation, but prior prompt engineering is understudied. |
| Approach: | They investigate prior prompt engineering (pPE) in reinforcement fine-tuning . they translate five representative iPE strategies into corresponding pPE approaches . |
| Outcome: | The proposed approaches outperform iPE-prompted models on in-domain and out-of-domain benchmarks. |
Null-Shot Prompting: Rethinking Prompting Large Language Models With Hallucination (2024.emnlp-main)
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| Challenge: | a series of investigations into an interesting phenomenon where performance increases in large language models when providing a prompt that causes and exploits hallucination. |
| Approach: | They propose a null-shot prompting approach that intentionally instructs LLMs to look at and utilize information from a nil section. |
| Outcome: | The proposed approach causes and exploits hallucination in large language models on a range of tasks including arithmetic reasoning, commonsense reasoning, and reading comprehension. |
Extending Audio Context for Long-Form Understanding in Large Audio-Language Models (2026.eacl-long)
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Yuatyong Chaichana, Pittawat Taveekitworachai, Warit Sirichotedumrong, Potsawee Manakul, Kunat Pipatanakul
| Challenge: | Prior work has introduced context-extension methods (e.g. YaRN) on unimodal LLMs, yet their application to LALMs remains unexplored. |
| Approach: | They propose a training-free, modality-decoupled extension method that modifies only audio token positions, leaving text positions intact to preserve the base LLM’s text capabilities. |
| Outcome: | The proposed method outperforms the original models across wide range of settings and provides significant performance improvement on long audio of unseen lengths. |