mlsanityData to 7 Oct 2026

Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation

arXiv 2604.03174 ↗First posted 3 Apr 2026cs.CL cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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Abstract (arXiv)

Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite context windows, and weakly structured causal reasoning. This survey provides a unified account of augmentation strategies along a single axis: the degree of structured context supplied at inference time. We cover in-context learning and prompt engineering, Retrieval-Augmented Generation (RAG), GraphRAG, and CausalRAG. Beyond conceptual comparison, we provide a transparent literature-screening protocol, a claim-audit framework, and a structured cross-paper evidence synthesis that distinguishes higher-confidence findings from emerging results. The paper concludes with a deployment-oriented decision framework and concrete research priorities for trustworthy retrieval-augmented NLP.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 19 of its 26 references that are in the map. First posted on arXiv 3 Apr 2026.

Builds on

19 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Models6 Oct

Other references · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  6. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  7. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  8. 2019Language Models as Knowledge Bases?cited by 1,922
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  10. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  11. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  12. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
6 more
  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  3. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  5. 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 51
  6. 2025CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generationcited by 12

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  1. 2025"Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models
  2. 2025A Survey of Context Engineering for Large Language Models
  3. 2024From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
  4. 2025Grounding Long-Context Reasoning with Contextual Normalization for Retrieval-Augmented Generation
  5. 2024More Room for Language: Investigating the Effect of Retrieval on Language Models
  6. 2024Inference Scaling for Long-Context Retrieval Augmented Generation
  7. 2024Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style
  8. 2024Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis