Search Results for author:
Found 23 papers, 15 papers with code
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
Scientific Research, vital for improving human life, is hindered by its inherent complexity, slow pace, and the need for specialized experts.
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA).
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks
Many existing works tackle this problem by generating synthetic data from the training data and then training models on them, recently using Large Language Models (LLMs).
Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Specifically, we construct an entity-centric knowledge store for each user based on their search and browsing activities on the web, which is then leveraged to provide contextually relevant LLM prompt augmentations.
Test-Time Self-Adaptive Small Language Models for Question Answering
Moreover, further finetuning LMs with labeled datasets is often infeasible due to their absence, but it is also questionable if we can transfer smaller LMs having limited knowledge only with unlabeled test data.
Knowledge-Augmented Language Model Verification
Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters.
Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning
To address this problem, we further introduce a novel contrastive learning strategy, making sure to reflect previous turns when retrieving the phrase for the current context, by maximizing representational similarities of consecutive turns in a conversation while minimizing irrelevant conversational contexts.
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
We validate the performance of our KAPING framework on the knowledge graph question answering task, that aims to answer the user's question based on facts over a knowledge graph, on which ours outperforms relevant zero-shot baselines by up to 48% in average, across multiple LLMs of various sizes.
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG.
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
Large Language Models (LLMs) have shown promising performance in knowledge-intensive reasoning tasks that require a compound understanding of knowledge.
Direct Fact Retrieval from Knowledge Graphs without Entity Linking
There has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks.
Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement
Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times.
Object Detection in Aerial Images with Uncertainty-Aware Graph Network
To achieve this, we first detect objects and then measure their semantic and spatial distances to construct an object graph, which is then represented by a graph neural network (GNN) for refining visual CNN features for objects.
Personalized Subgraph Federated Learning
To this end, we introduce a new subgraph FL problem, personalized subgraph FL, which focuses on the joint improvement of the interrelated local GNNs rather than learning a single global model, and propose a novel framework, FEDerated Personalized sUBgraph learning (FED-PUB), to tackle it.
KALA: Knowledge-Augmented Language Model Adaptation
Pre-trained language models (PLMs) have achieved remarkable success on various natural language understanding tasks.
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
Dense retrieval models, which aim at retrieving the most relevant document for an input query on a dense representation space, have gained considerable attention for their remarkable success.
Ranked #1000000000 on
Passage Retrieval
on Natural Questions
Graph Self-supervised Learning with Accurate Discrepancy Learning
Contrastive learning, while it can learn global graph-level similarities, its objective to maximize the similarity between two differently perturbed graphs may result in representations that cannot discriminate two similar graphs with different properties.
Edge Representation Learning with Hypergraphs
This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges.
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training.
Task-Adaptive Neural Network Search with Meta-Contrastive Learning
To address such limitations, we introduce a novel problem of \emph{Neural Network Search} (NNS), whose goal is to search for the optimal pretrained network for a novel dataset and constraints (e. g. number of parameters), from a model zoo.
Accurate Learning of Graph Representations with Graph Multiset Pooling
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.
Ranked #1 on
Graph Classification
on ToxCast
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities.
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests
Also, we analyze how neural models solve spatial reasoning tests with visual aids.
