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Found 6 papers, 6 papers with code
RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox Testing
We present RepoST, a scalable method to construct environments that provide execution feedback for repository-level code generation for both training and evaluation.
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations.
CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
To adequately test modern code generation systems, evaluation benchmarks must execute and test the code generated by the system.
Rationale-Guided Few-Shot Classification to Detect Abusive Language
We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains.
A Unified Framework for Pun Generation with Humor Principles
We propose a unified framework to generate both homophonic and homographic puns to resolve the split-up in existing works.
CABACE: Injecting Character Sequence Information and Domain Knowledge for Enhanced Acronym and Long-Form Extraction
Acronyms and long-forms are commonly found in research documents, more so in documents from scientific and legal domains.
