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Found 10 papers, 2 papers with code
Rethinking Data Selection for Supervised Fine-Tuning
Although supervised finetuning (SFT) has emerged as an essential technique to align large language models with humans, it is considered superficial, with style learning being its nature.
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models
This work strives to understand the proficiency of LLMs in logical reasoning by offering a brief review of the latest progress in this area; with a focus on the logical reasoning datasets, tasks, and the methods adopted to utilize LLMs for reasoning.
Fast and Automatic 3D Modeling of Antenna Structure Using CNN-LSTM Network for Efficient Data Generation
In this study, we proposed a deep learning-assisted and image-based intelligent modeling approach for accelerating the data acquisition of antenna samples with different physical structures.
Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network
We propose to use a liquid time constant (LTC) network to predict the future blockage status of a millimeter wave (mmWave) link using only the received signal power as the input to the system.
Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays using Baseband Signal
One key communication block in 5G and 6G radios is the active phased array (APA).
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews.
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance.
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction
In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.
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