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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Improving Translational Accuracy02:07

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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Related Experiment Video

Updated: Jan 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Beyond Fine-Tuning: Leveraging Domain-Aware In-Context learning with large language models for clinical named entity

Siun Kim1, David Seung U Lee2, Yujin Kim2

  • 1Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea.

Journal of Biomedical Informatics
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Optimized in-context learning (ICL) with large language models (LLMs) matches or exceeds encoder fine-tuning for clinical named entity recognition (NER). This approach offers efficient adaptation and continuous improvement in healthcare settings without retraining.

Keywords:
Clinical named entity recognitionCross-domain generalizationCross-institutional transferIn-context learningLarge language models

Related Experiment Videos

Last Updated: Jan 13, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Area of Science:

  • Natural Language Processing
  • Biomedical Informatics
  • Machine Learning

Background:

  • Clinical named entity recognition (NER) is crucial for organizing unstructured clinical text.
  • While large language models (LLMs) offer parameter-free adaptation via in-context learning (ICL), encoder-based fine-tuning has traditionally led in performance for clinical NER.
  • This study systematically compares ICL and fine-tuning under realistic conditions to assess their effectiveness.

Purpose of the Study:

  • To conduct a systematic comparison of ICL and encoder-based fine-tuning for clinical NER.
  • To evaluate the impact of optimizing ICL demonstration selection on performance.
  • To determine the viability of ICL in resource-constrained clinical settings.

Main Methods:

  • Utilized 2,113 annotated clinical notes from hematologic malignancy patients and 400 MIMIC-IV notes.
  • Optimized ICL configurations including instructions, output formats, and demonstration selection strategies using LLaMA-3.3-70B.
  • Performed encoder fine-tuning with RoBERTa-large as a baseline, evaluating all models on token-level classification across various scenarios (in-domain, cross-domain, cross-institutional).

Main Results:

  • Optimized demonstration selection significantly improved ICL performance, increasing macro F1 by up to 9.4 points.
  • In moderate settings, ICL outperformed RoBERTa-large fine-tuning and remained competitive with larger sample pools.
  • ICL demonstrated superior data efficiency and achieved substantial gains in cross-institutional transfer without parameter updates, though fine-tuning led at the largest pool size.

Conclusions:

  • Optimized domain-aware demonstration selection allows open-source LLM-based ICL to equal or surpass encoder fine-tuning for clinical NER.
  • ICL's adaptability and knowledge update capability through demonstration pools, without retraining, are advantageous for dynamic, resource-limited healthcare environments.
  • This facilitates continuous improvement in clinical NLP tasks within the healthcare sector.