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Found 9 papers, 7 papers with code
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
In StateFlow, we distinguish between "process grounding" (via state and state transitions) and "sub-task solving" (through actions within a state), enhancing control and interpretability of the task-solving procedure.
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Through conducting extensive experiments on a large scale of harmful and safe prompts, we validate the effectiveness of the proposed AutoDefense in improving the robustness against jailbreak attacks, while maintaining the performance at normal user request.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models.
An Empirical Study on Challenging Math Problem Solving with GPT-4
Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields.
HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts
In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data.
Improving Policy Optimization with Generalist-Specialist Learning
Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations.
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds.
Size-to-depth: A New Perspective for Single Image Depth Estimation
To overcome these problems, we propose a new perspective for single monocular image depth estimation problem: size to depth.
