Search Results for author:
Found 26 papers, 16 papers with code
AgentRM: Enhancing Agent Generalization with Reward Modeling
Based on this finding, we propose AgentRM, a generalizable reward model, to guide the policy model for effective test-time search.
WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
Finally, we merge the synthetic samples that pass quality confirmation with the collected samples to obtain the WorkflowBench.
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs
These experiments reveal that while most current models are robust against the "lost in the middle" issue, there exist significant biases related to the spacing of relevant information pieces.
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents.
Learning Evolving Tools for Large Language Models
Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs.
Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
While Large Vision-Language Models (LVLMs) have rapidly advanced in recent years, the prevalent issue known as the `hallucination' problem has emerged as a significant bottleneck, hindering their real-world deployments.
Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain Translation
H&E-to-IHC stain translation techniques offer a promising solution for precise cancer diagnosis, especially in low-resource regions where there is a shortage of health professionals and limited access to expensive equipment.
MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series
Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels.
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging.
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.
GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
As GitHub has hosted a multitude of repositories which can be seen as a good resource for tools, a promising solution is that LLM-based agents can autonomously integrate the repositories in GitHub according to the user queries to extend their tool set.
Seeing Is Not Always Believing: Invisible Collision Attack and Defence on Pre-Trained Models
The typical paradigm is to pre-train a big deep learning model on large-scale data sets, and then fine-tune the model on small task-specific data sets for downstream tasks.
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models
Pre-trained language models (PLMs) are known to be overly parameterized and have significant redundancy, indicating a small degree of freedom of the PLMs.
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge.
Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection
To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.
Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide.
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health.
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting
In Inter-Series Embedding Module, a multi-scale unified convolution component called Region-Aware Convolution is proposed, which cooperates with self-attention to capture dynamic dependencies between time series obtained from multiple regions.
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification
Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied.
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.
Person Re-Identification Using Heterogeneous Local Graph Attention Networks
Specifically, we first construct the completed local graph using local features, and we resort to the attention mechanism to aggregate the local features in the learning process of inter-local relation and intra-local relation so as to emphasize the importance of different local features.
Revisiting Representation Degeneration Problem in Language Modeling
They call it the representation degeneration problem and propose a cosine regularization to solve it.
Cross-View Action Recognition via a Continuous Virtual Path
In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which connects the source view and the target view.
Scene Text Recognition Using Part-Based Tree-Structured Character Detection
Scene text recognition has inspired great interests from the computer vision community in recent years.
