Search Results for author:
Found 21 papers, 6 papers with code
StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation
Furthermore, although many prompt-based methods have been proposed to accomplish specific tasks, their performance in complex real-world scenarios involving a wide variety of dialog styles further enhancement.
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint
Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking.
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine.
Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection
Detecting leaks in water networks is a costly challenge.
Weakly Supervised Regression with Interval Targets
Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval.
Partial-Label Regression
Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses.
RWKV: Reinventing RNNs for the Transformer Era
This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.
Ranked #22 on
Natural Language Inference
on WNLI
Decouple knowledge from parameters for plug-and-play language modeling
The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM.
A Topic-aware Summarization Framework with Different Modal Side Information
To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.
Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round.
Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
Neural Machine Translation with Contrastive Translation Memories
Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios.
Optimal Measurement of Drone Swarm in RSS-based Passive Localization with Region Constraints
In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated.
Providing Location Information at Edge Networks: A Federated Learning-Based Approach
Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication cost.
Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array
In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP.
Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array
Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a high or ultra-high complexity.
Federated Learning-Based Localization with Heterogeneous Fingerprint Database
Fingerprint-based localization plays an important role in indoor location-based services, where the position information is usually collected in distributed clients and gathered in a centralized server.
Communication-efficient Coordinated RSS-based Distributed Passive Localization via Drone Cluster
Simulation results show that the proposed DMM performs better than the existing distributed Gauss-Newton method (DGN) in terms of root of mean square error (RMSE) under a limited low communication overhead constraint.
Enhanced RSS-based UAV Localization via Trajectory and Multi-base Stations
First, fixing the nth BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule.
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations.
