Search Results for author:
Found 24 papers, 16 papers with code
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
Supported by benchmark results and the rise of open-source implementations, we demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans.
A Survey on Large Language Model based Human-Agent Systems
Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
The high cost of obtaining high-quality annotated data for in-context learning (ICL) has motivated the development of methods that use self-generated annotations in place of ground-truth labels.
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Collaborative filtering models, particularly graph-based approaches, have demonstrated strong performance in capturing user-item interactions for recommendation systems.
TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data Consistency
Test-time computing approaches, which leverage additional computational resources during inference, have been proven effective in enhancing large language model performance.
GLEAN: Generalized Category Discovery with Diverse and Quality-Enhanced LLM Feedback
Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories.
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety.
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
We demonstrate for the first time that prompting a fixed LLM can yield high-quality synthetic tabular data.
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations.
COOD: Concept-based Zero-shot OOD Detection
Existing OOD detection methods struggle to capture the intricate semantic relationships and label co-occurrences inherent in multi-label settings, often requiring large amounts of training data and failing to generalize to unseen label combinations.
Sequential LLM Framework for Fashion Recommendation
The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience.
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
However, these approaches are often validated with traditional benchmarks and early-stage LLMs, leaving uncertainty about their effectiveness when evaluating state-of-the-art LLMs on the contamination of more challenging benchmarks.
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world applications.
PersonaGym: Evaluating Persona Agents and LLMs
Persona agents, which are LLM agents conditioned to act according to an assigned persona, enable contextually rich and user aligned interactions across domains like education and healthcare.
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks
Specifically, we query the model to generate a fallacious yet deceptively real procedure for the harmful behavior.
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years.
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction.
Mixed Supervised Graph Contrastive Learning for Recommendation
Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM
To address these issues, we introduce EIVEN, a data- and parameter-efficient generative framework that pioneers the use of multimodal LLM for implicit attribute value extraction.
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations.
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support.
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification
However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation.
