Search Results for author:
Found 16 papers, 8 papers with code
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space.
Ranked #45 on
Arithmetic Reasoning
on GSM8K
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Common Sense Reasoning
on BIG-bench (Known Unknowns)
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.
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains.
Ranked #1 on
Text-To-SQL
on KaggleDBQA
Programming Puzzles
The dataset is comprehensive in that it spans problems of a range of difficulties and domains, ranging from trivial string manipulation problems, to classic programming puzzles (e. g., Tower of Hanoi), to interview/competitive-programming problems (e. g., dynamic programming), to longstanding open problems in algorithms and mathematics (e. g., factoring).
Structure-Grounded Pretraining for Text-to-SQL
Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation.
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
Ranked #9 on
Semantic Parsing
on spider
ADAPTIVE GENERATION OF PROGRAMMING PUZZLES
Puzzles are objective in that one can easily test the correctness of a given solution x by seeing whether it satisfies f, unlike the most common representations for program synthesis: given input-output pairs or an English problem description, the correctness of a given solution is not determined and is debatable.
Program Synthesis and Semantic Parsing with Learned Code Idioms
Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time.
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.
Robust Text-to-SQL Generation with Execution-Guided Decoding
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.
Generative Code Modeling with Graphs
Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs.
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
In this work, we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique that combines the best of both symbolic logic techniques and statistical models.
FlashProfile: A Framework for Synthesizing Data Profiles
However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios.
Learning Syntactic Program Transformations from Examples
In the second domain, we use repetitive edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code.
