Synthetic network generators preserve cross-flow correlations enabling source-level membership inference, shown via the TraceBleed attack across five datasets and six generators.
Language mod- els are few-shot learners
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
CodeMind evaluates ten LLMs on four benchmarks using three new code reasoning tasks, finding performance varies by model size and drops with complexity while showing no correlation with bug repair ability.
Presents a hierarchical energy-aware framework with UCB-DUAL bandit for decentralized rank scheduling in multi-task federated fine-tuning for IoV networks.
RouteFormer is a transformer-RL hybrid for single-agent graph routing that reports 10% and 7% shorter distances than Concorde and LKH-3 on mission-like graphs by incorporating constraints the solvers ignore.
MulFSA combines micro-level firm sentiment, meso-level industry sentiment, and duration-aware smoothing from PLMs/LLMs to extract a daily sentiment index that reduces credit spread forecast errors by 10.25% MAE and 11.94% MAPE on a 1.35M-text Chinese bond corpus.
Survey of 188 engineers using SEM finds that UTAUT2 constructs influence LLM adoption differently across five SE purposes, with some factors showing negative effects when examined in isolation.
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
citing papers explorer
-
Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces
Synthetic network generators preserve cross-flow correlations enabling source-level membership inference, shown via the TraceBleed attack across five datasets and six generators.
-
CodeMind: Evaluating Large Language Models for Code Reasoning
CodeMind evaluates ten LLMs on four benchmarks using three new code reasoning tasks, finding performance varies by model size and drops with complexity while showing no correlation with bug repair ability.
-
Decentralized Rank Scheduling for Energy-Constrained Multi-Task Federated Fine-Tuning in Edge-Assisted IoV Networks
Presents a hierarchical energy-aware framework with UCB-DUAL bandit for decentralized rank scheduling in multi-task federated fine-tuning for IoV networks.
-
RouteFormer: A Transformer-Based Routing Framework for Autonomous Vehicles
RouteFormer is a transformer-RL hybrid for single-agent graph routing that reports 10% and 7% shorter distances than Concorde and LKH-3 on mission-like graphs by incorporating constraints the solvers ignore.
-
MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market
MulFSA combines micro-level firm sentiment, meso-level industry sentiment, and duration-aware smoothing from PLMs/LLMs to extract a daily sentiment index that reduces credit spread forecast errors by 10.25% MAE and 11.94% MAPE on a 1.35M-text Chinese bond corpus.
-
Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering Purposes
Survey of 188 engineers using SEM finds that UTAUT2 constructs influence LLM adoption differently across five SE purposes, with some factors showing negative effects when examined in isolation.
-
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.