mlsanityData to 14 Oct 2026

Effects of Prompt Length on Domain-specific Tasks for Large Language Models

arXiv 2502.14255 ↗First posted 20 Feb 2025cs.CL cs.AI cs.ETcited by 11

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Abstract (arXiv)

In recent years, Large Language Models have garnered significant attention for their strong performance in various natural language tasks, such as machine translation and question answering. These models demonstrate an impressive ability to generalize across diverse tasks. However, their effectiveness in tackling domain-specific tasks, such as financial sentiment analysis and monetary policy understanding, remains a topic of debate, as these tasks often require specialized knowledge and precise reasoning. To address such challenges, researchers design various prompts to unlock the models' abilities. By carefully crafting input prompts, researchers can guide these models to produce more accurate responses. Consequently, prompt engineering has become a key focus of study. Despite the advancements in both models and prompt engineering, the relationship between the two-specifically, how prompt design impacts models' ability to perform domain-specific tasks-remains underexplored. This paper aims to bridge this research gap.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 20 Feb 2025.

Builds on

30 of 52 references.

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 6.

11 papers.

  1. 2025Understanding Prompt Management in GitHub Repositories: A Call for Best Practicescited by 5
  2. 2026AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generationcited by 2
  3. 2025Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commercecited by 1
  4. 2025Brewing Knowledge in Context: Distillation Perspectives on In-Context Learningcited by 1
  5. 2025CienaLLM: Generative Climate-Impact Extraction from News Articles with Autoregressive LLMscited by 0
  6. 2025Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implicationscited by 0
  7. 2026Implicit Framing in Obstetric Counseling Notes: A Grounded LLM Pipeline on a VBAC-Eligible Cohortcited by 0
  8. 2026MuMuTestUp: Mutation-based Multi-Agent Test Case Updatecited by 0
  9. 2026Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolutioncited by 0
  10. 2026Performance of large language models in the optical diagnosis of colorectal polypscited by 0
  11. 2026Lost in Translation: Measuring the Effect of Non-Native English on End User Performance of Large Language Modelscited by 0