Search Results for author:
Found 15 papers, 5 papers with code
Towards Better Text-to-Image Generation Alignment via Attention Modulation
To achieve this, we incorporate a temperature control mechanism within the early phases of the self-attention modules to mitigate entity leakage issues.
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation
We investigate this approach under two different settings: one where the policy model is smaller and is paired with a more powerful critic model, and another where a single language model fulfills both roles.
Plug-in Diffusion Model for Sequential Recommendation
To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items.
Fusion-Eval: Integrating Evaluators with LLMs
Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning.
SiRA: Sparse Mixture of Low Rank Adaptation
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks.
Critique Ability of Large Language Models
In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks.
Towards an On-device Agent for Text Rewriting
Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting.
Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models.
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side.
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting
In this work, we develop new strategies for instruction tuning and reinforcement learning to better align LLMs for cross-sentence rewriting tasks using diverse wording and structures expressed through natural languages including 1) generating rewriting instruction data from Wiki edits and public corpus through instruction generation and chain-of-thought prompting; 2) collecting comparison data for reward model training through a new ranking function.
Triple Sequence Learning for Cross-domain Recommendation
To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR.
Multi-modal Video Chapter Generation
On top of this dataset, we design an effective baseline specificlly for video chapters generation task.
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features.
Multi-source Domain Adaptation for Visual Sentiment Classification
Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data.
Pairwise versus multiple network alignment
Just as the recent trend in the NA field, we also focus on global NA, which can be pairwise (PNA) and multiple (MNA).
