arXiv AI
ICLHerd: Herding-Based Data Selection for LLMs with In-Context Learning as a Proxy
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arXiv Machine Learning
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How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
arXiv:2510. 01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to adapt to new tasks from only a handful of examples.
arXiv Machine Learning
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LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering
arXiv:2604. 03532v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult.
arXiv Machine Learning
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Data Efficient Sample Selection for In-Context Learning
arXiv:2609.06670v1 Announce Type: new Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an opti...
arXiv Machine Learning
Sep 22
Weak Ties, Strong Signals: Efficient Training Data Detection in Diffusion LLMs via Independent Token Sampling
The paper introduces Independent Token Sampling (ITS), a query‑efficient method for detecting memorized training data in diffusion large language models (dLLMs). ITS selects token sets with weak internal dependency by approximating cumulative conditional mutual information using an attention‑derived pairwise dependency proxy and promotes diversity across sampling rounds. Experiments show ITS outperforms existing baselines, improving AUC by 0.18 on the ArXiv dataset while remaining effective under limited query budgets.
arXiv Machine Learning
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Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs
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arXiv Computation and Language
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DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models
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