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Found 12 papers, 2 papers with code
Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems
Retrieval-Augmented Generation (RAG) is a pivotal technique for enhancing the capability of large language models (LLMs) and has demonstrated promising efficacy across a diverse spectrum of tasks.
LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation
This highlights the need for a reliable testing and evaluation platform.
LeCov: Multi-level Testing Criteria for Large Language Models
Large Language Models (LLMs) are widely used in many different domains, but because of their limited interpretability, there are questions about how trustworthy they are in various perspectives, e. g., truthfulness and toxicity.
MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems
If an unsafe action is detected, MORTAR then initiates a repair process to correct it.
AcTracer: Active Testing of Large Language Model via Multi-Stage Sampling
Given that LLMs' diverse task-handling abilities stem from large volumes of training data, a comprehensive evaluation also necessitates abundant, well-annotated, and representative test data to assess LLM performance across various downstream tasks.
Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture
As safety remains a crucial concern throughout the development lifecycle of Large Language Models (LLMs), researchers and industrial practitioners have increasingly focused on safeguarding and aligning LLM behaviors with human preferences and ethical standards.
GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model
To address this issue, we introduce in this work a novel Generalizable Safety enhancer (GenSafe) that is able to overcome the challenge of data insufficiency and enhance the performance of SRL approaches.
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
To bridge this gap, we conduct in this work a comprehensive evaluation of the effectiveness of existing online safety analysis methods on LLMs.
LUNA: A Model-Based Universal Analysis Framework for Large Language Models
the trustworthiness perspective, is bound to and enriches the abstract model with semantics, which enables more detailed analysis applications for diverse purposes.
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
Then, the performance of the reward function is assessed, and the results are presented back to the LLM for guiding its self-refinement process.
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics.
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
In particular, we experiment with twelve uncertainty estimation methods and four LLMs on four prominent natural language processing (NLP) tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs.
