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Found 36 papers, 19 papers with code
A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base
Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs).
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion
Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs.
LLMIdxAdvis: Resource-Efficient Index Advisor Utilizing Large Language Model
Index recommendation is essential for improving query performance in database management systems (DBMSs) through creating an optimal set of indexes under specific constraints.
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases.
LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model
Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity.
Personalized Clustering via Targeted Representation Learning
In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e. g., $\textit{must-link}$ or $\textit{cannot-link}$ pairs) to guide the clustering direction.
SAM Decoding: Speculative Decoding via Suffix Automaton
Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration.
P$^2$ Law: Scaling Law for Post-Training After Model Pruning
Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs).
PCQPR: Proactive Conversational Question Planning with Reflection
Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment.
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance.
A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering
In order to improve the generalization capabilities of KBQA models, extensive research has embraced a retrieve-then-reason framework to retrieve relevant evidence for logical expression generation.
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
Gradient leakage has been identified as a potential source of privacy breaches in modern image processing systems, where the adversary can completely reconstruct the training images from leaked gradients.
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
This tuner can directly recommend promising configurations for any new workload, eliminating the need for the extensive workload replays required by previous approaches.
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB.
Streamlining Redundant Layers to Compress Large Language Models
This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs).
Open-World Semi-Supervised Learning for Node Classification
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.
A Survey on Neural Question Generation: Methods, Applications, and Prospects
In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images.
CodeS: Towards Building Open-source Language Models for Text-to-SQL
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
FOSS: A Self-Learned Doctor for Query Optimizer
They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints.
Diversifying Question Generation over Knowledge Base via External Natural Questions
The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity.
$\rm SP^3$: Enhancing Structured Pruning via PCA Projection
Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension critical to model size and efficiency.
Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch
Specifically, WAD captures adaptive weights and high-quality pseudo labels to target instances by exploring point mutual information (PMI) in representation space to maximize the role of unlabeled data and filter unknown categories.
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
The generalization problem on KBQA has drawn considerable attention.
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i. e., tables and columns) and the skeleton (i. e., SQL keywords).
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Superclass Learning With Representation Enhancement
In many real scenarios, data are often divided into a handful of artificial super categories in terms of expert knowledge rather than the representations of images.
Oracle-guided Contrastive Clustering
Accordingly, the query results, guided by oracles with distinctive demands, may drive the OCC's clustering results in a desired orientation.
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning.
An Accelerator for Rule Induction in Fuzzy Rough Theory
First, a rule induction method based on consistence degree, called Consistence-based Value Reduction (CVR), is proposed and used as basis to accelerate.
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation
In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items.
Self-supervised Graph Learning for Occasional Group Recommendation
As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations.
Contrastive Coding for Active Learning Under Class Distribution Mismatch
However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data.
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models.
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
Cold-start problem is a fundamental challenge for recommendation tasks.
