Papers by Elita Lobo
On the Impact of Fine-Tuning on Chain-of-Thought Reasoning (2025.naacl-long)
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| Challenge: | Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities. |
| Approach: | They propose to use supervised fine-tuning and Quantized Low-Rank Adapters to improve LLMs' task-specific performance to address privacy and safety risks. |
| Outcome: | The proposed model improves the accuracy of the chain-of-thought reasonings across four datasets and demonstrates that the faithfulness of CoT reasoning decreases. |