REVIEW 6 cited by
Understanding LLMs: A Comprehensive Overview from Training to Inference
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.
Forward citations
Cited by 6 Pith papers
-
Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges
Per-bias selection of a cross-family LLM auditor lifts biased-judgment accuracy from 0.805/0.824 baselines to 0.884.
-
Carbon- and Precedence-Aware Scheduling for Data Processing Clusters
A carbon-aware Spark scheduler that defers low-priority tasks during high-carbon periods, using importance scores from an ML scheduler, reduced carbon by roughly a third in a 100-node prototype with near-neutral end-t...
-
Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
A MILP scheduler that jointly optimizes GPU composition, deployment configuration, and workload assignment reports 20-41% cost-efficiency gains over homogeneous GPU clusters for LLM serving.
-
AI Governance through Markets
Market governance mechanisms, supported by standardized AI disclosures, can create financial incentives for responsible AI development, according to this policy paper.
-
A Survey: Towards Privacy and Security in Mobile Large Language Models
A survey of privacy and security challenges for mobile large language models, summarizing known attack types and defenses without introducing new results.
-
Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations
A narrative review asserting that LLMs transform marketing with personalization and automation, but without new evidence or rigorous analysis.
Discussion (0). Continue with ORCID to comment.