Summary of Prompt-efficient Fine-tuning For Gpt-like Deep Models to Reduce Hallucination and to Improve Reproducibility in Scientific Text Generation Using Stochastic Optimisation Techniques, by Daniil Sulimov
Prompt-Efficient Fine-Tuning for GPT-like Deep Models to Reduce Hallucination and to Improve Reproducibility in Scientific Text Generation Using Stochastic Optimisation Techniques
by Daniil Sulimov
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Parameter-Efficient Fine-Tuning (PEFT) approach addresses limitations in Large Language Models (LLMs) for complex text generation tasks, such as mass spectrometry applications. The PEFT method fine-tunes GPT-like models using Low-Rank Adaptation (LoRA) adapters and a specialized corpus of mass spectrometry literature. This results in superior text coherence and reproducibility compared to the baseline model, confirmed through statistical analysis with the Wilcoxon rank-sum test. The research also proposes a new metric for evaluating reproducibility based on cosine similarity of model outputs under controlled prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are getting better at writing scientific texts, but they often make mistakes or repeat themselves. This paper introduces a new way to make these models work better, called Parameter-Efficient Fine-Tuning (PEFT). The idea is to use a special type of adapter to refine the model’s performance on specific tasks, like generating text about mass spectrometry. By using this approach, the researchers were able to create a more accurate and reliable model that can be used for scientific writing. |
Keywords
* Artificial intelligence * Cosine similarity * Fine tuning * Gpt * Lora * Low rank adaptation * Parameter efficient * Text generation
