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Found 10 papers, 2 papers with code
Improving the Robustness of Large Language Models via Consistency Alignment
The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources.
Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph
Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology.
HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation
In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR.
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization
This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning.
MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR
To address APR accuracy and reduce VIO drift, MobileARLoc creates a feedback loop where VIO pose estimations refine the APR predictions.
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Dialogue assessment plays a critical role in the development of open-domain dialogue systems.
KS-APR: Keyframe Selection for Robust Absolute Pose Regression
However, APR methods tend to yield significant inaccuracies for input images that are too distant from the training set.
Robust Localization with Visual-Inertial Odometry Constraints for Markerless Mobile AR
However, VIO only tracks the relative pose of the device, leading to drift over time.
Feature-Level Debiased Natural Language Understanding
We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples.
