Search Results for author:
Found 14 papers, 9 papers with code
Self-Explore to Avoid the Pit: Improving the Reasoning Capabilities of Language Models with Fine-grained Rewards
Training on large amounts of rationales (i. e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs).
Semiparametric Token-Sequence Co-Supervision
Especially, the robustness of parametric token space which is established during the pretraining step tends to effectively enhance the stability of nonparametric sequence embedding space, a new space established by another language model.
Joint Mechanical and Electrical Adjustment of IRS-aided LEO Satellite MIMO Communications
In this letter, we propose a joint mechanical and electrical adjustment of intelligent reflecting surface (IRS) for the performance improvements of low-earth orbit (LEO) satellite multiple-input multiple-output (MIMO) communications.
Adaptive Shortcut Debiasing for Online Continual Learning
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment.
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
We identify the inadequacy of universal and specific prompting in handling these dynamic shifts.
How Well Do Large Language Models Truly Ground?
Reliance on the inherent knowledge of Large Language Models (LLMs) can cause issues such as hallucinations, lack of control, and difficulties in integrating variable knowledge.
Energy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement
With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust line-of-sight (LoS) connections with ground nodes owing to their free mobility and high altitude, especially for emergency and disaster response.
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction.
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2. 24% (Flan-T5 3B) and +2. 37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13. 98% margin.
Ranked #1 on
on BIG-bench (SNARKS)
Common Sense Reasoning
Common Sense Reasoning (Zero-Shot)
+7
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
Ranked #9 on
Question Answering
on StoryCloze
Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt
Enhancing the zero-shot performance of instruction-following models requires heavy computation, either by scaling the total number of training datasets or the model size.
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance.
Ranked #2 on
Question Answering
on StoryCloze
Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation
We compared the performance between the auto-detection system and the human eye.
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks
In our network, we utilize an average ensemble among multiple parts as a final feature to capture various temporal dependencies.
Ranked #107 on
Skeleton Based Action Recognition
on NTU RGB+D
