69 citations · 127 across the 16 of their papers we have counts for
17 papers
Attention-Aware Routing: Coupling Routing and Attention in MoEs
Despoina Kosmopoulou, Anastasios Tsetsilas, Efthymios Georgiou +3
In Mixture-of-Experts language models, the router typically selects and weights experts based on the token's hidden state, utilizing limited contextual information. We propose Atte…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
Yuhang Zhou, Giannis Karamanolakis, Victor Soto +5
The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert…
Interactive Machine Teaching by Labeling Rules and Instances
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Weakly supervised learning aims to reduce the cost of labeling data by using expert-designed labeling rules. However, existing methods require experts to design effective rules in…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance
Tejit Pabari, Beth Tellman, Giannis Karamanolakis +11
Floods cause large losses to property, life, and livelihoods across the world every year, hindering sustainable development. Safety nets to help absorb financial shocks in disaster…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
Guoqing Zheng, Giannis Karamanolakis, Kai Shu +1
Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of label…
Self-Training with Weak Supervision
Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng +1
State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of d…
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only
Ziyi Liu, Giannis Karamanolakis, Daniel Hsu +1
Health departments have been deploying text classification systems for the early detection of foodborne illness complaints in social media documents such as Yelp restaurant reviews…
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages. Existing approaches…
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Xin Luna Dong, Xiang He, Andrey Kan +19
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question a…
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Giannis Karamanolakis, Jun Ma, Xin Luna Dong
Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for…
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different asp…
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, ap…
Item Recommendation with Variational Autoencoders and Heterogenous Priors
Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan +3
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation o…
Audio-based Distributional Semantic Models for Music Auto-tagging and Similarity Measurement
Giannis Karamanolakis, Elias Iosif, Athanasia Zlatintsi +2
The recent development of Audio-based Distributional Semantic Models (ADSMs) enables the computation of audio and lexical vector representations in a joint acoustic-semantic space.…