Search Results for author:
Found 30 papers, 25 papers with code
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages
In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
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.
Process Reinforcement through Implicit Rewards
While dense rewards also offer an appealing choice for the reinforcement learning (RL) of LLMs since their fine-grained rewards have the potential to address some inherent issues of outcome rewards, such as training efficiency and credit assignment, this potential remains largely unrealized.
EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents
To address this, we propose EmbodiedEval, a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks.
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing.
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System
Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving, yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
Our results underscore that the capabilities of LLMs in handling structured data are still under-explored, and show the effectiveness of LLM4Graph in enhancing LLMs' proficiency of graph analysis.
AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems
However, configuring an MAS for a task remains challenging, with performance only observable post-execution.
Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents.
Autonomous Agents for Collaborative Task under Information Asymmetry
Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange.
Multi-Agent Software Development through Cross-Team Collaboration
We anticipate that our work will guide LLM agents towards a cross-team paradigm and contribute to their significant growth in but not limited to software development.
Scaling Large Language Model-based Multi-Agent Collaboration
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning.
Iterative Experience Refinement of Software-Developing Agents
We propose two fundamental patterns: the successive pattern, refining based on nearest experiences within a task batch, and the cumulative pattern, acquiring experiences across all previous task batches.
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication
Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of Large Language Models (LLMs).
Experiential Co-Learning of Software-Developing Agents
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents.
D-Bot: Database Diagnosis System using Large Language Models
Database administrators (DBAs) play an important role in managing, maintaining and optimizing database systems.
Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and memory costs without significant performance compromise.
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost.
ChatDev: Communicative Agents for Software Development
Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing.
Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning
Since it is non-trivial to directly model the intermediate states and design a running cost function, we propose to use latent stochastic bridges to regularize the intermediate states and use the regularization as the running cost of PETs.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
Exploring Mode Connectivity for Pre-trained Language Models
(3) How does the PLM's task knowledge change along the path connecting two minima?
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning
To fathom the mystery, we hypothesize that the adaptations of different DETs could all be reparameterized as low-dimensional optimizations in a unified optimization subspace, which could be found by jointly decomposing independent solutions of different DETs.
GACT: Activation Compressed Training for Generic Network Architectures
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
Exploring Universal Intrinsic Task Subspace via Prompt Tuning
In the experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 250-dimensional subspace found with 100 tasks, by only tuning 250 free parameters, we can recover 97% and 83% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace.
Fully Hyperbolic Neural Networks
Hyperbolic neural networks have shown great potential for modeling complex data.
Quantifying Similarity between Relations with Fact Distribution
We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases.
