Papers by Shaojie Shi
Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness (2023.emnlp-industry)
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| Challenge: | Recent studies have shown that large language models are useful, honest, harmless (HHH) however, RLHF requires high hardware resources and human efforts. |
| Approach: | They propose a framework that allows LLMs to align themselves with HHH . they use IF and reinforcement learning from human feedback to fine-tune their models . |
| Outcome: | The proposed framework achieves similar performance to RLHF and human-generated models with a minimal alignment tax. |
ULMR: Unlearning Large Language Models via Negative Response and Model Parameter Average (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) have attracted significant interest from the research community due to their broad applicability in many language-oriented tasks. |
| Approach: | They propose a framework which uses pre-training datasets to rewrite instructions and generate negative responses to preserve the performance of the original LLM. |
| Outcome: | The proposed framework can erase the pre-training data while maintaining the performance of the original model. |
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching (2023.emnlp-industry)
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| Challenge: | Low-Rank Adaptation (LoRA) has been used to adapt Large Language Models to a variety of tasks, but it requires substantial computational resources to perform. |
| Approach: | They propose a low-rank adaptive learning approach that leverages LoRA's in-context learning capability through prompt matching via reinforcement learning in resource-constrained environments. |
| Outcome: | The proposed model improves LoRA performance on evaluation metrics and utilises consumer-grade GPU resources. |