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Found 15 papers, 9 papers with code
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
Applying Reinforcement Learning (RL) to Video Large Language Models (Video-LLMs) shows significant promise for complex video reasoning.
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Large Multimodal Reasoning Models (LMRMs) have emerged as a promising paradigm, integrating modalities such as text, images, audio, and video to support complex reasoning capabilities and aiming to achieve comprehensive perception, precise understanding, and deep reasoning.
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects.
Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering
Experimental results demonstrate that RopMura effectively handles both single-hop and multi-hop queries, with the routing mechanism enabling precise answers for single-hop queries and the combined routing and planning mechanisms achieving accurate, multi-step resolutions for complex queries.
Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning
In this paper, we address this limitation by introducing a novel structure-oriented analysis method to help LLMs better understand the question and guide the problem-solving process of LLMs.
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
Specifically, our method involves the server generating a compressed LLM and aligning its performance with the full model.
Improving LoRA in Privacy-preserving Federated Learning
Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency.
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives.
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.
FS-Real: Towards Real-World Cross-Device Federated Learning
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry.
Differentially Private Vertical Federated Clustering
To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data.
Estimating Numerical Distributions under Local Differential Privacy
When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator.
Locally Differentially Private Frequency Estimation with Consistency
In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values.
Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability
We introduce a new concept, data irrecoverability, and show that the well-studied concept of data privacy is sufficient but not necessary for data irrecoverability.
