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Found 9 papers, 4 papers with code
EXAONE 3.0 7.8B Instruction Tuned Language Model
We introduce EXAONE 3. 0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research.
Re3val: Reinforced and Reranked Generative Retrieval
Subsequently, we extract and rerank contexts from the KILT database using the rerank page titles.
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online.
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
Notably, utilizing 'opposite' as the noisy instruction in ID, which exhibits the maximum divergence from the original instruction, consistently produces the most significant performance gains across multiple models and tasks.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning
In this paper, we present a risk-based exploration that leads to collaboratively optimistic behavior by shifting the sampling region of distribution.
Distributional Reinforcement Learning
Multi-agent Reinforcement Learning
+4
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Cell segmentation is a fundamental task for computational biology analysis.
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Risk Perspective Exploration in Distributional Reinforcement Learning
Distributional reinforcement learning demonstrates state-of-the-art performance in continuous and discrete control settings with the features of variance and risk, which can be used to explore.
Distributional Reinforcement Learning
reinforcement-learning
+3
