Search Results for author:
Found 45 papers, 23 papers with code
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.
CodeT: Code Generation with Generated Tests
A natural way to evaluate the quality and correctness of a code solution is to run it against a set of test cases, but the manual creation of such test cases is often costly and time-consuming.
Ranked #1 on
Code Generation
on APPS
(Introductory Pass@1 metric)
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository.
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context
Recently semantic parsing in context has received considerable attention, which is challenging since there are complex contextual phenomena.
Compositional Generalization by Learning Analytical Expressions
Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily.
Incomplete Utterance Rewriting as Semantic Segmentation
Recent years the task of incomplete utterance rewriting has raised a large attention.
Ranked #1 on
Dialogue Rewriting
on Rewrite
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Learning Algebraic Recombination for Compositional Generalization
In this paper, we propose LeAR, an end-to-end neural model to learn algebraic recombination for compositional generalization.
Ranked #2 on
Semantic Parsing
on CFQ
Reasoning Like Program Executors
Reasoning over natural language is a long-standing goal for the research community.
Ranked #2 on
Question Answering
on DROP Test
(using extra training data)
Does Deep Learning Learn to Abstract? A Systematic Probing Framework
Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.
You Impress Me: Dialogue Generation via Mutual Persona Perception
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors.
Ranked #2 on
Dialogue Generation
on Persona-Chat
(using extra training data)
TAPEX: Table Pre-training via Learning a Neural SQL Executor
TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL executor on the diverse, large-scale and high-quality synthetic corpus.
Ranked #1 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation
Usually, expensive text-code paired data is essential for training a code generation model.
Ranked #121 on
Code Generation
on HumanEval
When Language Model Meets Private Library
In this paper, we investigate how to equip pre-trained language models with the ability of code generation for private libraries.
SoTaNa: The Open-Source Software Development Assistant
To meet the demands of this dynamic field, there is a growing need for an effective software development assistant.
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.
Can Programming Languages Boost Each Other via Instruction Tuning?
Results demonstrate that programming languages can significantly improve each other.
FANDA: A Novel Approach to Perform Follow-up Query Analysis
NLIDB allow users to search databases using natural language instead of SQL-like query languages.
A Split-and-Recombine Approach for Follow-up Query Analysis
To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information.
Castor: Contextual IoT Time Series Data and Model Management at Scale
The main features of Castor are: (1) an efficient pipeline for ingesting IoT time series data in real time; (2) a scalable, hybrid data management service for both time series and contextual data; (3) a versatile semantic model for contextual information which can be easily adopted to different application domains; (4) an abstract framework for developing and storing predictive models in R or Python; (5) deployment services which automatically train and/or score predictive models upon user-defined conditions.
Computation Other Statistics
Question Answering as Programming for Solving Time-Sensitive Questions
Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world.
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.
One button machine for automating feature engineering in relational databases
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects.
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Our approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction.
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features.
Jointly Modeling Topics and Intents with Global Order Structure
Modeling document structure is of great importance for discourse analysis and related applications.
(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories
Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems.
Learning-to-Ask: Knowledge Acquisition via 20 Questions
Almost all the knowledge empowered applications rely upon accurate knowledge, which has to be either collected manually with high cost, or extracted automatically with unignorable errors.
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions
Conditional Markov Chains (also known as Linear-Chain Conditional Random Fields in the literature) are a versatile class of discriminative models for the distribution of a sequence of hidden states conditional on a sequence of observable variables.
Statistical and machine learning ensemble modelling to forecast sea surface temperature
Training data consisted of satellite-derived SST and atmospheric data from The Weather Company.
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL
One key component in text-to-SQL is to predict the comparison relations between columns and their values.
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games.
Discovering Traveling Companions using Autoencoders
The ability to discover moving objects that travel together, i. e., traveling companions, from their trajectories is desired by many applications such as intelligent transportation systems and location-based services.
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization
Human intelligence exhibits compositional generalization (i. e., the capacity to understand and produce unseen combinations of seen components), but current neural seq2seq models lack such ability.
AutoAI-TS: AutoAI for Time Series Forecasting
We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset.
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
This motivates us to propose input-tuning, which fine-tunes both the continuous prompts and the input representations, leading to a more effective way to adapt unfamiliar inputs to frozen PLMs.
UniDU: Towards A Unified Generative Dialogue Understanding Framework
With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU).
Multi-task Learning for Paraphrase Generation With Keyword and Part-of-Speech Reconstruction
The rationale is to capture simultaneously the possible keywords of a source sentence and the relations between them to facilitate the rewriting.
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks.
Making Large Language Models Better Reasoners with Step-Aware Verifier
Few-shot learning is a challenging task that requires language models to generalize from limited examples.
Ranked #49 on
Arithmetic Reasoning
on GSM8K
Large Language Models Meet NL2Code: A Survey
The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence.
How Do In-Context Examples Affect Compositional Generalization?
Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence.
Skill-Based Few-Shot Selection for In-Context Learning
Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning.
