The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
Better Zero-Shot Reasoning with Role-Play Prompting , rights =
8 Pith papers cite this work, alongside 25 external citations. Polarity classification is still indexing.
8
Pith papers citing it
25
external citations · Pith
abstract
Modern large language models (LLMs) exhibit a remarkable capacity for role-playing, enabling them to embody not only human characters but also non-human entities. This versatility allows them to simulate complex human-like interactions and behaviors within various contexts, as well as to emulate specific objects or systems. While these capabilities have enhanced user engagement and introduced novel modes of interaction, the influence of role-playing on LLMs' reasoning abilities remains underexplored. In this study, we introduce a strategically designed role-play prompting methodology and assess its performance under the zero-shot setting across twelve diverse reasoning benchmarks. Our empirical results illustrate that role-play prompting consistently surpasses the standard zero-shot approach across most datasets. Notably, in experiments conducted using ChatGPT, accuracy on AQuA rises from 53.5% to 63.8%, and on Last Letter from 23.8% to 84.2%.Upon further comparison with the Zero-Shot-CoT technique, which prompts the model to "think step by step", our study demonstrates that role-play prompting acts as a more effective trigger for the CoT process. This highlights its potential to augment the reasoning capabilities of LLMs. We release our code at https://github.com/NKU-HLT/Role-Play-Prompting.
citation-role summary
background 2
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
HyPE integrates DRL mobility prediction, LLM-based UAV trajectory and inference assignment, and greedy heuristics for service placement and routing to jointly optimize latency, output fidelity, and personalization continuity in UAV-assisted 6G networks.
MADE is a new multilingual agentic diagnosing engine that produces higher-quality diagnostic reports (47% better than baseline) on a large-scale evaluation substrate covering 33 model families and 26 languages.
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
PICCO is a five-element reference architecture (Persona, Instructions, Context, Constraints, Output) for structuring LLM prompts, derived from synthesizing prior frameworks along with a taxonomy distinguishing prompt concepts.
By steering activations along directions learned from base-vs-fine-tuned disagreements, REFLEX reports 64.99 macro-F1 on RAW-FC fact-checking with only ~465 self-refined contrastive samples and no retrieval.
Empirical tests on four GPT models across five uncertainty types found hyper-truth states (T+I+F>1) in 35% of cases, mostly under ethical contradictions and paradoxes.
citing papers explorer
-
Guidelines for Empirical Studies in Software Engineering involving Large Language Models
The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
-
Quality-Aware Personalized AI Service Provisioning in UAV-Assisted 6G Networks
HyPE integrates DRL mobility prediction, LLM-based UAV trajectory and inference assignment, and greedy heuristics for service placement and routing to jointly optimize latency, output fidelity, and personalization continuity in UAV-assisted 6G networks.
-
MADE: Beyond Scoring via a Multilingual Agentic Diagnosing Engine for Fine-Grained Evaluation Insights
MADE is a new multilingual agentic diagnosing engine that produces higher-quality diagnostic reports (47% better than baseline) on a large-scale evaluation substrate covering 33 model families and 26 languages.
-
Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.
-
Towards Explorative IRBL: Combining Semantic Retrieval with LLM-driven Iterative Code Exploration
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
-
The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure
PICCO is a five-element reference architecture (Persona, Instructions, Context, Constraints, Output) for structuring LLM prompts, derived from synthesizing prior frameworks along with a taxonomy distinguishing prompt concepts.
-
REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control
By steering activations along directions learned from base-vs-fine-tuned disagreements, REFLEX reports 64.99 macro-F1 on RAW-FC fact-checking with only ~465 self-refined contrastive samples and no retrieval.
-
Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models
Empirical tests on four GPT models across five uncertainty types found hyper-truth states (T+I+F>1) in 35% of cases, mostly under ethical contradictions and paradoxes.