--- title: "Apple" description: "Large American technology company that makes popular consumer electronics, computer software, and online services." --- # Apple > Large American technology company that makes popular consumer electronics, computer software, and online services. ## Articles ### [Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions](https://yomu.fyi/post/multilingual-knowledge-transfer-under-data-constraints-via-lexical-int.md) - Company: [Apple](https://yomu.fyi/company/apple.md) - Published: Aug 20, 2026 - Type: Problem & solution Cross-lingual knowledge transfer is critical for training multilingual language models when target-language data is scarce and downstream tasks demand reasoning or world knowledge. Existing transfer approaches often depend on scarce resources such as large parallel corpora, translation systems, auxiliary models, or extra training phases. To address this limitation, the authors introduce LINK, a data-level intervention method that operates directly during pretraining via bilingual vocabularies. LINK swaps randomly selected words in a portion of the high-resource English corpus with word-level translations according to a specified replacement ratio. Across evaluations spanning eight languages and five model sizes, the technique delivered notable performance gains on downstream target-language tasks while achieving up to a 2x speedup in training to reach equivalent performance. ### [Scaling Laws for Mixture Pretraining Under Data Constraints](https://yomu.fyi/post/scaling-laws-for-mixture-pretraining-under-data-constraints.md) - Company: [Apple](https://yomu.fyi/company/apple.md) - Published: Aug 20, 2026 - Type: Problem & solution Scaling language models demands large volumes of data, but high-value target sources like low-resource languages and specialized domains remain inherently scarce. Practitioners frequently combine limited target data with abundant generic data, creating a balance between domain underexposure and excessive repetition leading to overfitting. An empirical evaluation across more than 2,000 language-model training runs demonstrates that mixture training tolerates substantially higher data repetition than single-source training, allowing scarce target corpora to be repeated 15 to 20 times. Researchers introduced a repetition-aware mixture scaling law that accounts for both the diminishing returns of repeated target tokens and the regularizing effect of generic data. Optimizing this scaling law offers a principled framework for calculating effective mixture configurations and practical pretraining recommendations under severe data constraints. ### [Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR](https://yomu.fyi/post/progressive-refinement-an-iterative-pseudo-labeling-approach-for-manda.md) - Company: [Apple](https://yomu.fyi/company/apple.md) - Published: Aug 20, 2026 - Type: Problem & solution Code-switching automatic speech recognition faces major performance challenges due to the scarcity of code-switching training data. To mitigate this data limitation, an iterative pseudo-labeling framework leverages a large unlabeled audio corpus to generate semi-supervised speech datasets. The system structure executes across three sequential phases: pseudo-label generation, two-stage bilingual model pre-training with supervised code-switching fine-tuning, and progressive iterative improvements. Successive refinement cycles further enhance the neural model's recognition accuracy when processing complex code-switching scenarios across alternating languages. When evaluated on the SEAME benchmark, this training pipeline achieved Mix Error Rate reductions of 6.35 percent on the devman subset and 8.29 percent on the devsge subset. ### [Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts](https://yomu.fyi/post/examining-human-like-behaviors-in-llms-a-multi-dimensional-analysis-of.md) - Company: [Apple](https://yomu.fyi/company/apple.md) - Published: Aug 19, 2026 - Type: Benchmark Large language models frequently exhibit human-like behaviors such as expressing emotions, building relationships, and setting boundaries, yet empirical guidance on appropriate usage remains limited. To address this gap, researchers analyzed 21,000 multi-turn conversations across four models: gpt-4o, gpt-4.1-mini, claude-sonnet-4.6, and gemini-2.5-flash. The study combined LLM-as-a-judge techniques with human evaluation to measure behavior prevalence, perceived appropriateness, and prompt controllability across diverse user goals and profiles. Human evaluators judged LLM self-referential and relationship-building behaviors as less appropriate than identical human actions, whereas boundary-maintaining behaviors were rated as more appropriate for models. System prompting effectively steered these behaviors, though the authors emphasize that rigorous evaluation remains necessary to prevent unintended side effects. ### [The P-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs](https://yomu.fyi/post/the-p-completeness-of-inverted-index-traversal-on-the-complexity-of-ev.md) - Company: [Apple](https://yomu.fyi/company/apple.md) - Published: Aug 19, 2026 - Type: Explainer Modern artificial intelligence agents increasingly rely on search infrastructure to run complex, neuro-symbolic reasoning workflows that compile into deeply nested, non-monotonic Boolean queries. Standard inverted index evaluation methods struggle with these query structures, facing either exponential query complexity under Document-at-a-Time models or universe-scale space penalties under Term-at-a-Time models during logical negation. To address these theoretical limitations, the author formalizes a DAG-based retrieval language named L\_R and proves its query evaluation problem is strictly P-Complete. The paper introduces ComputePN, a deterministic and sparsity-aware algorithm that employs a Positive-Negative dual representation alongside native DAG memoization. This mechanism bounds evaluation time to O(|Q| ยท |U\_active|), effectively evaluating P-Complete queries natively over the index while bypassing combinatorial tree expansion and universal scan overhead. [Newer posts](https://yomu.fyi/company/apple/page/2.md)