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Found 12 papers, 4 papers with code
EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories
Existing benchmarks demonstrate poor alignment with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.
SEED: Customize Large Language Models with Sample-Efficient Adaptation for Code Generation
Specifically, SEED involves identifying error code generated by LLMs, employing Self-revise for code revision, optimizing the model with revised code, and iteratively adapting the process for continuous improvement.
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models
CDD necessitates only the sampled texts to detect data contamination, by identifying the peakedness of LLM's output distribution.
DevEval: Evaluating Code Generation in Practical Software Projects
Compared to previous benchmarks, DevEval aligns to practical projects in multiple dimensions, e. g., real program distributions, sufficient dependencies, and enough-scale project contexts.
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts.
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels with any other party.
CodePAD: Sequence-based Code Generation with Pushdown Automaton
CodePAD can leverage existing sequence-based models, and we show that it can achieve 100\% grammatical correctness percentage on these benchmark datasets.
Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation
However, in some domain-specific scenarios, building such a large paired corpus for code generation is difficult because there is no directly available pairing data, and a lot of effort is required to manually write the code descriptions to construct a high-quality training dataset.
Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation
To evaluate the effectiveness of our proposed loss, we implement and train an Antecedent Prioritized Tree-based code generation model called APT.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition
Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals.
Efficient Algorithms for Rotation Averaging Problems
While a sufficient optimality condition is available in the literature, there is a lack of \yhedit{a} fast convergent algorithm to achieve stationary points.
SR2CNN: Zero-Shot Learning for Signal Recognition
This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation.
