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Notes on large language models (llms)

6 notes

  1. Improving Item Discoverability in e-Commerce Search via Related Intent Generation

    Source note · Jul 29, 2026 · large language models (llms)

    Researchers propose a new system for improving item discoverability in e-commerce searches. The system uses intent-conditioned recall expansion to generate implicit user intents, which helps to expand candidate recall…

  2. Linguistic Monoculture in LLM-Assisted Language Use

    Source note · Jul 29, 2026 · large language models (llms), mathematical modeling

    The paper discusses the potential impact of using large language models (LLMs) in writing and communication on linguistic diversity. It presents a mathematical framework to analyze how authors and LLMs evolve through…

  3. Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study

    Source note · Jul 23, 2026 · ai in healthcare, large language models (llms)

    Researchers propose a pipeline called CT-TEL that uses Large Language Models (LLMs) to convert narrative clinical trial protocols into a format called Temporal Ensemble Logic (TEL). This conversion allows for automated…

  4. Extending LLM Context via Associative Recurrent Memory

    Source note · Jul 13, 2026 · large language models (llms)

    In this research, the authors propose a new method called Associative Recurrent Memory Transformer (ARMT) to extend the context length of large language models (LLMs). This is crucial for many real-world applications…

  5. Future Confidence Distillation in Large Language Models

    Source note · Jul 8, 2026 · large language models (llms)

    In this research paper from 2026, Sahil Kale explores the importance of accurate confidence estimation in large language models (LLMs) for various applications like retrieval and tool use. The study focuses on how…

  6. Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study

    Source note · Jun 24, 2026 · large language models (llms)

    The authors propose an empirical study to evaluate the effectiveness and potential risks of using Large Language Models (LLMs) for vulnerability patching in software development. They plan to conduct a controlled…

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