VAnim creates open-domain text-to-SVG animations via sparse state updates on a persistent DOM tree, identification-first planning, and rendering-aware RL with a new 134k-example benchmark.
A survey of prompt engineering meth- ods in large language models for different nlp tasks
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.
COMPASS formalizes prompt engineering as a POMDP-based cognitive decision process for self-adaptive generation of task plan explanations via LLMs.
LLM reaches >=0.95 accuracy on 60 number theory problems with optimal hints; LightGBM classifier empirically supports Dirichlet conductor conjecture via zero features at 93.9% test accuracy for small q.
A systematic review that categorizes prompting strategies for LLM-based code summarization, assesses their effectiveness, and identifies gaps in research and evaluation practices.
Empirical tests on three LLMs show prompt semantics and task keywords drive inference energy costs more than length, with varying patterns by task.
citing papers explorer
-
VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation
VAnim creates open-domain text-to-SVG animations via sparse state updates on a persistent DOM tree, identification-first planning, and rendering-aware RL with a new 134k-example benchmark.
-
PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses
PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.
-
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
COMPASS formalizes prompt engineering as a POMDP-based cognitive decision process for self-adaptive generation of task plan explanations via LLMs.
-
Artificial Intelligence in Number Theory: LLMs for Algorithm Generation and Ensemble Methods for Conjecture Verification
LLM reaches >=0.95 accuracy on 60 number theory problems with optimal hints; LightGBM classifier empirically supports Dirichlet conductor conjecture via zero features at 93.9% test accuracy for small q.
-
Prompt-Driven Code Summarization: A Systematic Literature Review
A systematic review that categorizes prompting strategies for LLM-based code summarization, assesses their effectiveness, and identifies gaps in research and evaluation practices.
-
Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference
Empirical tests on three LLMs show prompt semantics and task keywords drive inference energy costs more than length, with varying patterns by task.
- MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval