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Found 30 papers, 13 papers with code
Investigating Answerability of LLMs for Long-Form Question Answering
As we embark on a new era of LLMs, it becomes increasingly crucial to understand their capabilities, limitations, and differences.
XGen-7B Technical Report
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
Exploring the Integration Strategies of Retriever and Large Language Models
This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation.
Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System
End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capabilities of pre-trained models.
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT
Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals.
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness.
Answering Complex Questions over Text by Hybrid Question Parsing and Execution
It comprises two central pillars: (1) We parse the question of varying complexity into an intermediate representation, named H-expression, which is composed of simple questions as the primitives and symbolic operations representing the relationships among them; (2) To execute the resulting H-expressions, we design a hybrid executor, which integrates the deterministic rules to translate the symbolic operations with a drop-in neural reader network to answer each decomposed simple question.
Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems
Upon the designed DNSC architecture, we further combine adversarial learning, variational autoencoder, and diffusion model to propose the Latent Diffusion DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising.
LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer
Graphic layout designs play an essential role in visual communication.
AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation
Dense retrievers have made significant strides in text retrieval and open-domain question answering, even though most achievements were made possible only with large amounts of human supervision.
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database
Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB).
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation
Finally, we fine-tune the model with limited data with true labels to fully adapt it to the target domain.
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text.
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking
Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not.
Compressed Predictive Information Coding
The key insight of our framework is to learn representations by minimizing the compression complexity and maximizing the predictive information in latent space.
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model
Stochastic linear mixing models (SLMM) assume the mixture coefficients depend on input, making them more flexible and effective to capture complex output dependence.
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models.
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
Faceted summarization provides briefings of a document from different perspectives.
Ranked #1 on
Unsupervised Extractive Summarization
on FacetSum
Unsupervised Deep Keyphrase Generation
Keyphrase generation aims to summarize long documents with a collection of salient phrases.
Predicting User Engagement Status for Online Evaluation of Intelligent Assistants
Therefore, we consider predicting user engagement status as the very first and critical step to online evaluation for intelligent assistants.
An Empirical Study on Neural Keyphrase Generation
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.
Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
Accurate interpretation of such prediction outcomes from a machine learning model that explicitly captures temporal correlations can significantly benefit the domain experts.
Nonstationary Multivariate Gaussian Processes for Electronic Health Records
We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the correlations between the observed output, are all time dependent.
Hierarchical Hidden Markov Jump Processes for Cancer Screening Modeling
In this paper, we propose a piece-wise stationary transition matrix to explain the heterogeneity in time.
Regularized Sparse Gaussian Processes
An issue faced by SGP, especially in latent variable models, is the inefficient learning of the inducing inputs, which leads to poor model prediction.
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation.
Integrating Transformer and Paraphrase Rules for Sentence Simplification
Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning.
Ranked #3 on
Text Simplification
on ASSET
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.
Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing
In our work, we propose a system, called crowdsourced subjective knowledge acquisition (CoSKA), for subjective knowledge acquisition powered by crowdsourcing and existing KBs.
Deep Keyphrase Generation
Keyphrase provides highly-condensed information that can be effectively used for understanding, organizing and retrieving text content.
