TL;DR
This systematic literature review (SLR) provides a comprehensive overview of pruning techniques applied to LLMs, based on 60 peer-reviewed studies and preprints published between 2022 and 2025, sourced from major digital libraries.
Whether contemporary LLMs can reproduce the research outcomes of a fully documented human study: a 1991 article that identified dermatophytosis (ringworm) in historical fine art was evaluated.
This chapter surveys 34 peer-reviewed papers applying generative LLMs to literature retrieval and the screening of candidate studies against eligibility criteria, identified via a Boolean search over the OpenAIRE Graph.
This tutorial provides a comprehensive and up-to-date overview of LLM mechanism discovery, validation, and editing, and surveys mechanistic editing techniques that leverage MI insights to modify behavior at varying granularity.
As the context window of Large Language Models (LLMs) continues to expand, the data required to effectively train and evaluate these capabilities remains underexplored. With existing research primarily focuses on architectural optimization, there is a need for a systematic, data-centric review. This survey bridges th...
Large language models (LLMs) have achieved remarkable success in high-resource languages, yet their performance on Traditional Mongolian remains highly limited. A primary bottleneck is the absence of a systematic evaluation framework, which precludes quantitative comparison and obscures directions for model optimizatio...
It is reported that the nonuniform semistructured framework detects critical layers and that, as demonstrated through successive pruning experiments, their preservation from pruning greatly reduces performance degradation, compared to a fully pruned model.
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