103 citations · 131 across the 10 of their papers we have counts for
7 papers · 1 filter
Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies
Nadav Timor, Jonathan Mamou, Daniel Korat +5
Accelerating the inference of large language models (LLMs) is a critical challenge in generative AI. Speculative decoding (SD) methods offer substantial efficiency gains by generat…
Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models
Jonathan Mamou, Oren Pereg, Daniel Korat +4
Speculative decoding is commonly used for reducing the inference latency of large language models. Its effectiveness depends highly on the speculation lookahead (SL)-the number of…
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
Phillip Howard, Arden Ma, Vasudev Lal +5
The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training an…
Efficient Few-Shot Learning Without Prompts
Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo +4
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, t…
TangoBERT: Reducing Inference Cost by using Cascaded Architecture
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
The remarkable success of large transformer-based models such as BERT, RoBERTa and XLNet in many NLP tasks comes with a large increase in monetary and environmental cost due to the…
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
Oren Pereg, Daniel Korat, Moshe Wasserblat +2
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction. The system is interpretable and user friendly and does not require labeled training da…
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set…