Pith. sign in

REVIEW 5 cited by

Understanding LLMs: A Comprehensive Overview from Training to Inference

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2401.02038 v2 pith:LNFQIGYF submitted 2024-01-04 cs.CL

Yiheng Liu , Hao He , Tianle Han , Xu Zhang , Mengyuan Liu , Jiaming Tian , Yutong Zhang , Jiaqi Wang
show 13 more authors
Xiaohui Gao Tianyang Zhong Yi Pan Shaochen Xu Zihao Wu Zhengliang Liu Xin Zhang Shu Zhang Xintao Hu Tuo Zhang Ning Qiang Tianming Liu Bao Ge
This is my paper · ORCID
classification cs.CL
keywords trainingllmsdeploymentinferencemodeldevelopmentfuturelanguage
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges

    cs.CL 2026-05 conditional novelty 6.0 of 10

    Per-bias selection of a cross-family LLM auditor lifts biased-judgment accuracy from 0.805/0.824 baselines to 0.884.

  2. Carbon- and Precedence-Aware Scheduling for Data Processing Clusters

    cs.DC 2025-02 conditional novelty 6.0 of 10

    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...

  3. Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs

    cs.DC 2025-02 conditional novelty 5.0 of 10

    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.

  4. AI Governance through Markets

    econ.GN 2025-01 conditional novelty 4.0 of 10

    Market governance mechanisms, supported by standardized AI disclosures, can create financial incentives for responsible AI development, according to this policy paper.

  5. A Survey: Towards Privacy and Security in Mobile Large Language Models

    cs.CR 2025-09 conditional

    A survey of privacy and security challenges for mobile large language models, summarizing known attack types and defenses without introducing new results.

Pith tools