17 citations · 26 across the 12 of their papers we have counts for
13 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
AI Research Preference Models
Thomas Simon Foster, Bassel Al Omari, Tingchen Fu +30
AI research agents (AIRA) can now carry machine learning experiments from proposal through implementation and evaluation. Yet progress on frontier tasks is throttled by the cost of…
Learning Visual Prompts for Guiding the Attention of Vision Transformers
Razieh Rezaei, Masoud Jalili Sabet, Jindong Gu +3
Visual prompting infuses visual information into the input image to adapt models toward specific predictions and tasks. Recently, manually crafted markers such as red circles are s…
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
Ayyoob Imani, Peiqin Lin, Amir Hossein Kargaran +8
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we cr…
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging
Ayyoob Imani, Silvia Severini, Masoud Jalili Sabet +2
Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled data for training. An established method for training a POS…
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
Silvia Severini, Viktor Hangya, Masoud Jalili Sabet +2
Bilingual Word Embeddings (BWEs) are one of the cornerstones of cross-lingual transfer of NLP models. They can be built using only monolingual corpora without supervision leading t…
CaMEL: Case Marker Extraction without Labels
Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet +1
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We prop…
Graph Algorithms for Multiparallel Word Alignment
Ayyoob Imani, Masoud Jalili Sabet, Lütfi Kerem Şenel +3
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research…
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus
Ayyoob Imani, Masoud Jalili Sabet, Philipp Dufter +2
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective. Researching typological prope…
Subword Sampling for Low Resource Word Alignment
Ehsaneddin Asgari, Masoud Jalili Sabet, Philipp Dufter +2
Annotation projection is an important area in NLP that can greatly contribute to creating language resources for low-resource languages. Word alignment plays a key role in this set…
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
Masoud Jalili Sabet, Philipp Dufter, François Yvon +1
Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do me…
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Nina Poerner, Masoud Jalili Sabet, Benjamin Roth +1
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora. We therefore present an alternative approach based on cross-lingu…
Low-dimensional Query Projection based on Divergence Minimization Feedback Model for Ad-hoc Retrieval
Javid Dadashkarimi, Masoud Jalili Sabet, Heshaam Faili +1
Low-dimensional word vectors have long been used in a wide range of applications in natural language processing. In this paper we shed light on estimating query vectors in ad-hoc r…