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Found 8 papers, 2 papers with code
A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation
LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions.
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment tax" -a compromise where enhancements in alignment within one objective (e. g., harmlessness) can diminish performance in others (e. g., helpfulness).
Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration
Although deep neural networks yield high classification accuracy given sufficient training data, their predictions are typically overconfident or under-confident, i. e., the prediction confidences cannot truly reflect the accuracy.
Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation
Moreover, our study identifies weaknesses in existing models' ability to repair vulnerable code, even when provided with vulnerability information.
Spatio-Temporal Branching for Motion Prediction using Motion Increments
Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses.
Toward Auto-evaluation with Confidence-based Category Relation-aware Regression
Auto-evaluation aims to automatically evaluate a trained model on any test dataset without human annotations.
BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information
Time is an important aspect of documents and is used in a range of NLP and IR tasks.
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections
In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets.
