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Found 14 papers, 2 papers with code
Solving Data-centric Tasks using Large Language Models
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users.
Assessing GPT4-V on Structured Reasoning Tasks
Multi-modality promises to unlock further uses for large language models.
FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language
Writing such rules is often challenging for users as it requires them to understand and implement the underlying logic.
CodeFusion: A Pre-trained Diffusion Model for Code Generation
Imagine a developer who can only change their last line of code, how often would they have to start writing a function from scratch before it is correct?
Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs
Inspired by prior work, we generate a collection of self-supervised structural tasks (e. g. navigate to a cell and row; transpose the table) and evaluate the performance differences when using 8 formats.
Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation
We investigate the role of generative AI models in providing human tutor-style programming hints to help students resolve errors in their buggy programs.
Co-audit: tools to help humans double-check AI-generated content
Hence, we are seeing the emergence of tool-assisted experiences to help the user double-check a piece of AI-generated content.
DataVinci: Learning Syntactic and Semantic String Repairs
DataVinci learns regular-expression-based patterns that cover a majority of values in a column and reports values that do not satisfy such patterns as data errors.
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
In our work, we systematically evaluate two models, ChatGPT (based on GPT-3. 5) and GPT-4, and compare their performance with human tutors for a variety of scenarios.
FLAME: A small language model for spreadsheet formulas
We evaluate FLAME on formula repair, formula completion, and similarity-based formula retrieval.
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming.
Repairing Bugs in Python Assignments Using Large Language Models
We propose to use a large language model trained on code, such as Codex, to build an APR system -- MMAPR -- for introductory Python programming assignments.
Repair Is Nearly Generation: Multilingual Program Repair with LLMs
We show that RING can outperform language-specific repair engines for three of these languages.
CORNET: Learning Table Formatting Rules By Example
Since we are the first to introduce conditional formatting, we compare CORNET to a wide range of symbolic and neural baselines adapted from related domains.
