English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce Aligner, a novel Parameter-Efficient Fine-Tuning (PEFT) methodfor aligning multi-billion-parameter-sized Large Language Models (LLMs).Aligner employs a unique design that constructs a globally shared set oftunable tokens that modify the attention of every layer. Remarkably with thismethod, even when using one token accounting for a mere 5,000 parameters,Aligner can still perform comparably well to state-of-the-art LLM adaptationmethods like LoRA that require millions of parameters. This capacity issubstantiated in both instruction following and value alignment tasks. Besidesthe multiple order-of-magnitude improvement in parameter efficiency, theinsight Aligner provides into the internal mechanisms of LLMs is also valuable.The architectural features and efficacy of our method, in addition to ourexperiments demonstrate that an LLM separates its internal handling of "form"and "knowledge" in a somewhat orthogonal manner. This finding promises tomotivate new research into LLM mechanism understanding and value alignment.

Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models