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Found 18 papers, 3 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.
Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
DeepFace-EMD (Phan et al. 2022) reaches state-of-the-art accuracy on out-of-distribution data by first comparing two images at the image level, and then at the patch level.
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?
TST$^\mathrm{R}$: Target Similarity Tuning Meets the Real World
Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance.
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.
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.
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.
Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example
After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.
Efficiently-Verifiable Strong Uniquely Solvable Puzzles and Matrix Multiplication
We advance the Cohn-Umans framework for developing fast matrix multiplication algorithms.
From Words to Code: Harnessing Data for Program Synthesis from Natural Language
Existing approaches have utilized data context in a limited way by simply adding relevant information from the input data into the prompts sent to the LLM.
FLAME: A small language model for spreadsheet formulas
We evaluate FLAME on formula repair, formula completion, and similarity-based formula retrieval.
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.
Neurosymbolic Repair for Low-Code Formula Languages
To address this problem, we developed LaMirage, a LAst-MIle RepAir-engine GEnerator that combines symbolic and neural techniques to perform last-mile repair in low-code formula languages.
Synchromesh: Reliable code generation from pre-trained language models
Then, Synchromesh feeds the examples to a pre-trained language model and samples programs using Constrained Semantic Decoding (CSD): a general framework for constraining the output to a set of valid programs in the target language.
Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis
Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description.
