Search Results for author:
Found 24 papers, 10 papers with code
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
This paper aims to improve our theoretical understanding of the generalization capabilities of the auto-regressive decoding retrieval paradigm, laying a foundation for its limitations and inspiring future advancements toward more robust and generalizable generative retrieval.
UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets
Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets.
Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning
This paper presents a combined approach to enhancing the effectiveness of Jacobian-Free Newton-Krylov (JFNK) method by deep reinforcement learning (DRL) in identifying fixed points within the 2D Kuramoto-Sivashinsky Equation (KSE).
ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers
As large language models (LLMs) grow in size, traditional full fine-tuning becomes increasingly impractical due to its high computational and storage costs.
Uncovering Overfitting in Large Language Model Editing
Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs).
Impact of ChatGPT on the writing style of condensed matter physicists
We apply a state-of-the-art difference-in-differences approach to estimate the impact of ChatGPT's release on the writing style of condensed matter papers on arXiv.
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing
Building on the validated hypothesis, we propose a novel knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE).
MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model
The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning.
Learning Domain-Invariant Features for Out-of-Context News Detection
In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time Adaptation) which applies contrastive learning and maximum mean discrepancy (MMD) to learn domain-invariant features.
ExcluIR: Exclusionary Neural Information Retrieval
However, in information retrieval community, there is little research on exclusionary retrieval, where users express what they do not want in their queries.
Generative Retrieval as Multi-Vector Dense Retrieval
Both methods compute relevance as a sum of products of query and document vectors and an alignment matrix.
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data.
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase.
Knowledge Graph Enhanced Large Language Model Editing
Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge.
Self-Supervised Position Debiasing for Large Language Models
Existing debiasing methods for LLMs require external bias knowledge or annotated non-biased samples, which is lacking for position debiasing and impractical in reality.
Patched Denoising Diffusion Models For High-Resolution Image Synthesis
Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space.
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning
Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns.
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration
To accomplish punctuation restoration, most existing methods focus on introducing extra information (e. g., part-of-speech) or addressing the class imbalance problem.
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
Deep Contrastive Multiview Network Embedding
In our work, different views can be obtained based on the various relations among nodes.
Dynamic Graph Neural Networks for Sequential Recommendation
We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
Dynamic Graph Collaborative Filtering
Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session.
