44 citations · 52 across the 7 of their papers we have counts for
8 papers
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
TweetNERD -- End to End Entity Linking Benchmark for Tweets
Shubhanshu Mishra, Aman Saini, Raheleh Makki +3
Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (N…
CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification
Vivek Kulkarni, Kenny Leung, Aria Haghighi
Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classif…
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
Ahmed El-Kishky, Thomas Markovich, Kenny Leung +3
Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate item…
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
Ahmed El-Kishky, Thomas Markovich, Serim Park +8
Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent o…
Learning Stance Embeddings from Signed Social Graphs
John Pougué-Biyong, Akshay Gupta, Aria Haghighi +1
A key challenge in social network analysis is understanding the position, or stance, of people in the graph on a large set of topics. While past work has modeled (dis)agreement in…
LMSOC: An Approach for Socially Sensitive Pretraining
Vivek Kulkarni, Shubhanshu Mishra, Aria Haghighi
While large-scale pretrained language models have been shown to learn effective linguistic representations for many NLP tasks, there remain many real-world contextual aspects of la…
Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction
Shubhanshu Mishra, Aria Haghighi
We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus by adding a pretraining task called translation pair prediction (TPP), wh…