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Found 33 papers, 17 papers with code
Distance Metric Learning with Joint Representation Diversification
Distance metric learning (DML) is to learn a representation space equipped with a metric, such that examples from the same class are closer than examples from different classes with respect to the metric.
See or Guess: Counterfactually Regularized Image Captioning
Image captioning, which generates natural language descriptions of the visual information in an image, is a crucial task in vision-language research.
Prototypical Reward Network for Data-Efficient RLHF
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs).
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
To address these limitations, we introduce the Retrieval Augmented Thought Tree (RATT), a novel thought structure that considers both overall logical soundness and factual correctness at each step of the thinking process.
Evaluating Concept-based Explanations of Language Models: A Study on Faithfulness and Readability
Based on this, we quantify faithfulness via the difference in the output upon perturbation.
Uncovering Safety Risks of Large Language Models through Concept Activation Vector
Despite careful safety alignment, current large language models (LLMs) remain vulnerable to various attacks.
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models
Furthermore, it is complementary to existing methods.
Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
In this paper, we propose a general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values
The rapid advancement of Large Language Models (LLMs) has attracted much attention to value alignment for their responsible development.
Unpacking the Ethical Value Alignment in Big Models
Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications.
Evaluating General-Purpose AI with Psychometrics
Comprehensive and accurate evaluation of general-purpose AI systems such as large language models allows for effective mitigation of their risks and deepened understanding of their capabilities.
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models.
Semi-Offline Reinforcement Learning for Optimized Text Generation
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications.
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality.
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
In this paper, we move towards combining large parametric models with non-parametric prototypical networks.
Self-explaining deep models with logic rule reasoning
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.
A Unified Understanding of Deep NLP Models for Text Classification
The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually.
ProFairRec: Provider Fairness-aware News Recommendation
To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.
Reinforcement Routing on Proximity Graph for Efficient Recommendation
Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.
Towards Fine-Grained Reasoning for Fake News Detection
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.
PENS: A Dataset and Generic Framework for Personalized News Headline Generation
To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS).
Learning Fair Representations for Recommendation: A Graph-based Perspective
For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user.
Interactive Steering of Hierarchical Clustering
The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven).
FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning
In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes.
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network
In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown.
A Neural Influence Diffusion Model for Social Recommendation
The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems.
Tracking Idea Flows between Social Groups
To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups.
Online Visual Analytics of Text Streams
To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes.
