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Found 12 papers, 3 papers with code
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic
While significant progress has been made in benchmarking Large Language Models (LLMs) across various tasks, there is a lack of comprehensive evaluation of their abilities in responding to multi-turn instructions in less-commonly tested languages like Arabic.
Scaling up Discovery of Latent Concepts in Deep NLP Models
Despite the revolution caused by deep NLP models, they remain black boxes, necessitating research to understand their decision-making processes.
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language.
LAraBench: Benchmarking Arabic AI with Large Language Models
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
Even though the models' RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior.
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
In this paper, we present PILOT -- a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network's plan, guaranteeing fulfilment of safety and comfort requirements.
PaRoT: A Practical Framework for Robust Deep Neural Network Training
Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation.
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions.
Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
This is achieved by first learning a set of visual {`}concepts{'} that abstract the visual feature spaces into concepts that have human-level meaning.
Estimating Activity at Multiple Scales using Spatial Abstractions
Autonomous robots operating in dynamic environments must maintain beliefs over a hypothesis space that is rich enough to represent the activities of interest at different scales.
Bayesian Policy Reuse
We formalise the problem of policy reuse, and present an algorithm for efficiently responding to a novel task instance by reusing a policy from the library of existing policies, where the choice is based on observed 'signals' which correlate to policy performance.
Clustering Markov Decision Processes For Continual Transfer
The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$.
