Search Results for author:
Found 12 papers, 2 papers with code
One Shot Learning as Instruction Data Prospector for Large Language Models
Nuggets assesses the potential of individual instruction examples to act as effective one shot examples, thereby identifying those that can significantly enhance diverse task performance.
Marathon: A Race Through the Realm of Long Context with Large Language Models
Although there are currently many benchmarks available for evaluating the long context understanding and reasoning capability of large language models, with the expansion of the context window in these models, the existing long context benchmarks are no longer sufficient for evaluating the long context understanding and reasoning capability of large language models.
A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions
Achieving an optimal equilibrium among these facets is crucial for maintaining the effectiveness and usability of FL systems while adhering to privacy and security standards.
TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework
Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime.
Federated Learning Incentive Mechanism under Buyers' Auction Market
Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners.
Iterative Forward Tuning Boosts In-context Learning in Language Models
In this paper, we propose an effective and efficient two-stage framework to boost ICL in LLMs by exploiting a dual form between Transformer attention and gradient descent-based optimization.
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases.
Ranked #1 on
Text-To-SQL
on BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
(Execution Accurarcy (Human) metric)
Matching-based Data Valuation for Generative Model
To the best of their knowledge, GMValuator is the first work that offers a training-free, post-hoc data valuation strategy for deep generative models.
Glass Segmentation Using Intensity and Spectral Polarization Cues
Transparent and semi-transparent materials pose significant challenges for existing scene understanding and segmentation algorithms due to their lack of RGB texture which impedes the extraction of meaningful features.
Ranked #14 on
Semantic Segmentation
on KITTI-360
A Survey of Toxic Comment Classification Methods
Yet, for people on the other side, toxic texts often lead to serious psychological consequences.
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
To realize such a goal, we propose AdaRec, a knowledge distillation (KD) framework which compresses knowledge of a teacher model into a student model adaptively according to its recommendation scene by using differentiable Neural Architecture Search (NAS).
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.
