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Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
Language and Cognition01:27

Language and Cognition

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.
Language Development01:22

Language Development

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Components of Language01:24

Components of Language

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. “eh”). Phonemes combine to...

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Related Experiment Video

Updated: Jul 10, 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

Tackling challenges in large language model-based data extraction via context engineering: A commentary on Jansen et

Junsong Lu1, X T XiaoTian Wang2

  • 1University of California-San Diego, Department of Psychology.

Psychological Bulletin
|July 9, 2026
PubMed
Summary

Large language models (LLMs) show promise for automating data extraction in systematic reviews but struggle with numerical data. Context engineering strategies can enhance LLM accuracy for research synthesis tasks.

Related Experiment Videos

Last Updated: Jul 10, 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

Area of Science:

  • Systematic Reviews & Meta-Analyses
  • Artificial Intelligence in Research

Background:

  • Data extraction in systematic reviews is labor-intensive and prone to errors.
  • Large language models (LLMs) offer potential for automating this process, improving efficiency and reliability.
  • Jansen et al. (2025) evaluated LLM accuracy in data extraction, finding success with study characteristics but limitations with numerical data like effect sizes.

Purpose of the Study:

  • To discuss current challenges in automated data extraction using LLMs.
  • To propose context engineering strategies for improving LLM performance in research synthesis.
  • To identify pathways for advancing LLM-assisted data extraction beyond basic instruction following.

Main Methods:

  • Analysis of challenges in LLM-assisted data extraction, including parsing semistructured data, long contexts, arithmetic induction, complex reasoning, and reproducibility.
  • Exploration of context engineering solutions like retrieval-augmented generation and tool-integrated reasoning.
  • Illustration of solutions with examples: OCR for semistructured data, function calls for effect sizes, LLM agents for retrieval, and self-refinement for output improvement.

Main Results:

  • LLMs demonstrate acceptable-to-good accuracy for study characteristic variables but struggle with numerical variables, particularly effect sizes.
  • Five key challenges were identified: parsing semistructured data, understanding long contexts, arithmetic induction, complex reasoning, and ensuring reproducibility.
  • Proposed solutions include retrieval-augmented generation, tool-integrated reasoning, OCR, function calls, LLM agents, and self-refinement.

Conclusions:

  • Automated data extraction using LLMs faces significant challenges, especially with numerical and complex data.
  • Context engineering offers a promising framework for enhancing LLM accuracy and reliability in research synthesis.
  • Future research should focus on developing advanced context engineering techniques for more robust automated data extraction.