Search Results for author:
Found 17 papers, 11 papers with code
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
The best setup, GPT-4 with chain of thought (CoT), achieves a pass@1 of 75% and 81% on input and output prediction, respectively.
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
In this work, we propose a new task of simulating NL feedback for interactive semantic parsing.
LEVER: Learning to Verify Language-to-Code Generation with Execution
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation.
Ranked #2 on
Semantic Parsing
on spider
Coder Reviewer Reranking for Code Generation
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.
Ranked #23 on
Code Generation
on MBPP
Natural Language to Code Translation with Execution
In this work, we introduce execution result--based minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it improves the few-shot performance of pretrained code models on natural-language-to-code tasks.
Ranked #37 on
Code Generation
on MBPP
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation
Mined bitexts can contain imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT).
SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark
We hope SILG enables the community to quickly identify new methodologies for language grounding that generalize to a diverse set of environments and their associated challenges.
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment
Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces.
Grounded Adaptation for Zero-shot Executable Semantic Parsing
We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e. g. new database schemas).
Ranked #6 on
Text-To-SQL
on SParC
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Ranked #32 on
Question Answering
on SQuAD1.1 dev
Naturalizing a Programming Language via Interactive Learning
Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases.
Data Noising as Smoothing in Neural Network Language Models
Data noising is an effective technique for regularizing neural network models.
Learning Language Games through Interaction
We introduce a new language learning setting relevant to building adaptive natural language interfaces.
Estimating Mixture Models via Mixtures of Polynomials
This framework allows us to draw insights and apply tools from convex optimization, computer algebra and the theory of moments to study problems in statistical estimation.
Relaxations for inference in restricted Boltzmann machines
We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field.
