LLM Expert-AI-powered assistant for research.

Example

LLM Expert suggests innovative ideas for research on LLMs, such as exploring the use of sparse attention mechanisms to improve model efficiency or creating domain-specific fine-tuned LLMs for niche applications like legal document processing or scientific literature analysis.

Scenario

A PhD student is looking for new topics to explore for their dissertation. LLM Expert provides a list of potential areas to explore, complete with relevant methodologies, dataset suggestions, and possible directions for experimentation.

Example

LLM Expert helps in designing experiments, such as recommending specific architectures, loss functions, and regularization techniques for improving model generalization. It may suggest the use of transformer-based architectures combined with curriculum learning for better handling of long-form text generation tasks.

Scenario

A researcher is developing an LLM-based system for generating code snippets. LLM Expert advises on integrating techniques like few-shot learning with retrieval-augmented generation to enhance performance while minimizing training costs.

Example

LLM Expert suggests relevant datasets such as WikiText, Common Crawl, or specific domain-based datasets like PubMed for training or fine-tuning LLMs on specialized tasks. It can also recommend benchmark tasks for evaluation, such as SuperGLUE or specific downstream tasks like question answering or summarization.

Scenario

A student working on natural language understanding tasks needs high-quality datasets to train and evaluate their model. LLM Expert identifies the best-suited datasets, suggests augmentation strategies, and offers guidance on evaluation metrics.

Example

LLM Expert provides guidance on conducting experiments within resource constraints, such as recommending model pruning, quantization techniques, or knowledge distillation to reduce model size without losing significant performance.

Scenario

A researcher with limited computational resources (8 A6000 GPUs) wants to experiment with large LLMs. LLM Expert proposes a strategy using mixed-precision training and leveraging transfer learning to make the experiments feasible within these constraints.

Example

LLM Expert can summarize the latest research papers and trends in the LLM space, offering insights into under-explored areas such as multimodal LLMs or prompt engineering for low-resource languages.

Scenario

A PhD student is looking to identify a gap in the literature on LLM interpretability. LLM Expert provides a concise analysis of recent works, highlighting trends and suggesting niche areas like explainability in large-scale multi-task LLMs.