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Found 41 papers, 18 papers with code
Investigating Factuality in Long-Form Text Generation: The Roles of Self-Known and Self-Unknown
Interestingly, even without significant changes in the models' self-judgment (Self-Known and Self-Unknown), the number of unsupported claims can increases, likely as an artifact of long-form generation.
CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval
This gap leaves existing models unable to effectively capture the diversity of programming languages and tasks across different domains, highlighting the need for more focused research in code retrieval.
TDGCN-Based Mobile Multiuser Physical-Layer Authentication for EI-Enabled IIoT
Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT).
Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models
Instruction-following capabilities in large language models (LLMs) have significantly progressed, enabling more complex user interactions through detailed prompts.
ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillation
Reranking documents based on their relevance to a given query is a critical task in information retrieval.
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
Embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering.
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review
While reasoning capabilities typically emerge in large language models (LLMs) with tens of billions of parameters, recent research focuses on improving smaller open-source models through knowledge distillation (KD) from commercial LLMs.
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models
Phrases are fundamental linguistic units through which humans convey semantics.
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
While most previous studies focus on the inter-event dependencies and their representations, how individual events influence the overall dynamics over time has been under-explored.
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques.
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding
Large Language Models (LLMs) have demonstrated a powerful ability for text generation.
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.
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
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
Furthermore, our proposed fingerprint selection algorithms achieve the reduction in the authentication error rate of up to 86. 93% compared to baseline active learning algorithms.
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.
HPE: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 and Domain Adaptation by Scalable Data Augmentation
In this study, we aim to develop unsupervised methods for improving dense retrieval models.
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
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.
