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Effects of Prompt Length on Domain-specific Tasks for Large Language Models
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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.
Forward citations
Cited by 4 Pith papers
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Zero-shot GPT-4o scoring of street-view façades can flag likely heritage buildings at national scale, but the threshold choices and validation are too weak to support use without expert oversight.
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When Compression Becomes an Attack Surface: Black-Box Attacks on Prompt-Compressed LLM Agents
The paper claims prompt compression is a new attack surface, but the abstract's COMA attack never appears in the body and the body's SoftCom requires white-box access.
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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning
In-context learning is reframed as implicit knowledge distillation, but the main claims either restate the known attention-equals-gradient-descent result or build the prompt-shift bound into the definition of MMD.
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