Papers by Lilian Ngweta
Aligners: Decoupling LLMs and Alignment (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. |
| Approach: | They propose to decouple LLMs and alignment by training *aligner* models that can be used to align any LLM on an as-needed basis. |
| Outcome: | The proposed model can be used to align any LLM for a given criteria on an as-needed basis. |
Towards LLMs Robustness to Changes in Prompt Format Styles (2025.naacl-srw)
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| Challenge: | Existing prompt engineering techniques for adapting pre-trained LLMs to specific tasks are zero-shot prompting and few-shot supervised fine-tuning. |
| Approach: | They propose a technique for addressing prompt brittleness by diversifying the styles used in the prompt few-shot examples by using computer vision techniques. |
| Outcome: | Empirical results show that the proposed technique reduces style-induced prompt brittleness while improving overall performance across prompt variations and different datasets. |