Language Identification with Succinct Machine-Independent Traces
Motivated by the power of large language models, there has been renewed interest in the Gold-Angluin model of language identification in the limit, with an eye toward variants of the model that might overcome the negative results for its original formulation. Recent papers on this question have proposed looking at computational traces and annotations of training strings as a source of additional power for a learner, reflecting empirical regularities such as the way that commented source code is easier to learn from than arbitrary source code, and text annotated with algorithmically generated c
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 61%chrisliu298/awesome-llm-unlearning →
- PossiblePossibly related (embedding) · 56%Furyton/awesome-language-model-analysis →
- PossiblePossibly related (embedding) · 52%Knowledge Distillation of Black-Box Large Language Models →
- PossiblePossibly related (embedding) · 51%wbopan/flashtrace →
- PossiblePossibly related (embedding) · 51%What exactly does word2vec learn? →
- LinkedLinked via arxiv author · 85%Moses Charikar →
“Language Identification with Succinct Machine-Independent Traces”
- LinkedLinked via arxiv author · 85%Jon Kleinberg →
“Language Identification with Succinct Machine-Independent Traces”
- LinkedLinked via arxiv author · 85%Chirag Pabbaraju →
“Language Identification with Succinct Machine-Independent Traces”