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Found 16 papers, 8 papers with code
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study
We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision.
Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack
Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration?
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
However, existing research primarily addresses only one type of noise, thereby limiting the effectiveness of noise reduction.
GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection
Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs.
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models
Recent advancements in language models (LMs) have gained substantial attentions on their capability to generate human-like responses.
The Impact of Downgrading Protected Areas (PAD) on Biodiversity
We quantitatively assess the impacts of Downgrading Protected Areas (PAD) on biodiversity in the U. S..
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
These components collaboratively tackle the trade-offs in on-device DRL training, improving timing and algorithm performance while minimizing the risk of out-of-memory (OOM) errors.
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas
Our work introduces a new angle for manipulation in recent multi-agent RL social dilemmas that utilize a unique reward function for incentivization.
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
In this paper, we propose MIMONet, a novel on-device multi-input multi-output (MIMO) DNN framework that achieves high accuracy and on-device efficiency in terms of critical performance metrics such as latency, energy, and memory usage.
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data
To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths.
White-Box Multi-Objective Adversarial Attack on Dialogue Generation
Such language models are, however, vulnerable to various adversarial samples as studied in traditional tasks such as text classification, which inspires our curiosity about their robustness in DG systems.
Estimating Predictive Uncertainty Under Program Data Distribution Shift
Experiments show that program distribution shift does degrade the DL model performance to varying degrees and that existing uncertainty methods all present certain limitations in quantifying uncertainty on program dataset.
GLIB: Towards Automated Test Oracle for Graphically-Rich Applications
We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99. 5\% recall in detecting non-crashing bugs such as game GUI glitches.
SHARE: a System for Hierarchical Assistive Recipe Editing
The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models.
Context-aware Attention Model for Coreference Resolution
Coreference resolution is an important task for gaining more complete understanding about texts by artificial intelligence.
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter
This paper describes the XJSA System submission from XJTU.
