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Found 56 papers, 36 papers with code
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models
Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences.
Discrete Diffusion Language Model for Long Text Summarization
While diffusion models excel at conditional generating high-quality images, prior works in discrete diffusion models were not evaluated on conditional long-text generation.
Encoding and Controlling Global Semantics for Long-form Video Question Answering
Seeking answers effectively for long videos is essential to build video question answering (videoQA) systems.
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
However, existing models suffer from repetitive topic and unassociated topic issues, failing to reveal the evolution and hindering further applications.
FASTopic: A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm
This brings about a neat and efficient topic modeling framework.
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning
Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results.
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models
Large language models (LLMs) have demonstrated multilingual capabilities; yet, they are mostly English-centric due to the imbalanced training corpora.
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.
Updating Language Models with Unstructured Facts: Towards Practical Knowledge Editing
In this paper, we propose a new benchmark, Unstructured Knowledge Editing (UKE).
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives.
Topic Modeling as Multi-Objective Contrastive Optimization
Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective.
Mercury: A Code Efficiency Benchmark for Code Large Language Models
Based on the distribution, we introduce a new metric Beyond, which computes a runtime-percentile-weighted Pass score to reflect functional correctness and code efficiency simultaneously.
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
In the face of uncertainty, the ability to *seek information* is of fundamental importance.
A Survey on Neural Topic Models: Methods, Applications, and Challenges
In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges.
LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training
Paraphrases are texts that convey the same meaning while using different words or sentence structures.
Graph Transformers for Large Graphs
As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations.
ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions
In the context-unaware setting, we evaluate ChatGPT in generating math questions for each lesson from pre-university math curriculums that we crawl.
From Static to Dynamic: A Continual Learning Framework for Large Language Models
The vast number of parameters in large language models (LLMs) endows them with remarkable capabilities, allowing them to excel in a variety of natural language processing tasks.
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.
Towards the TopMost: A Topic Modeling System Toolkit
Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling.
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System
This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.
Effective Neural Topic Modeling with Embedding Clustering Regularization
Topic models have been prevalent for decades with various applications.
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation
To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data.
Fact-Checking Complex Claims with Program-Guided Reasoning
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews.
Zero-Shot Text Classification via Self-Supervised Tuning
In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning.
InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method.
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning
To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more word co-occurrence information.
How to train your draGAN: A task oriented solution to imbalanced classification
The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago.
Vision-and-Language Pretraining
Moreover, a list of training datasets and downstream tasks is supplied to further polish the perspective into V\&L pretraining.
Long Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.
Ranked #3 on
Link Prediction
on PCQM-Contact
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
Certified Robustness Against Natural Language Attacks by Causal Intervention
Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.
Contrastive Learning for Neural Topic Model
Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample.
Graph Neural Networks with Learnable Structural and Positional Representations
An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.
Ranked #14 on
Graph Regression
on ZINC-500k
Grounding Language Representation with Visual Object Information via Cross Modal Pretraining
However, this approach has two main drawbacks: (i) the whole image usually contains more objects and backgrounds than the sentence itself; thus, matching them together will confuse the grounded model; (ii) CNN only extracts the features of the image but not the relationship between objects inside that, limiting the grounded model to learn complicated contexts.
Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.
Towards Robustness Against Natural Language Word Substitutions
Robustness against word substitutions has a well-defined and widely acceptable form, i. e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing.
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
Parameterization of Hypercomplex Multiplications
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding.
Benchmarking Graph Neural Networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
Ranked #1 on
Link Prediction
on COLLAB
Compositional De-Attention Networks
Attentional models are distinctly characterized by their ability to learn relative importance, i. e., assigning a different weight to input values.
Holographic Factorization Machines for Recommendation
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.
Holistic Multi-modal Memory Network for Movie Question Answering
In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources.
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension
Sequence encoders are crucial components in many neural architectures for learning to read and comprehend.
Ranked #7 on
Question Answering
on NarrativeQA
CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation
Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users.
Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis
Our novel model, \textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation.
Ranked #1 on
Recommendation Systems
on Netflix
(nDCG@10 metric)
