61 citations · 69 across the 12 of their papers we have counts for
12 papers · 1 filter
Agreement in Representation Space for Open-Ended Self-Consistency
Paula Ontalvilla, Gorka Azkune, Aitor Ormazabal
Self-consistency improves LLM reasoning by sampling multiple outputs and selecting the most consistent answer, but existing formulations largely rely on exact matching and therefor…
Multimodal LLMs Do Not Compose Skills Optimally Across Modalities
Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune
Skill composition is the ability to combine previously learned skills to solve new tasks. As neural networks acquire increasingly complex skills during their pretraining, it is not…
Improving the Efficiency of Visually Augmented Language Models
Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune
Despite the impressive performance of autoregressive Language Models (LM) it has been shown that due to reporting bias, LMs lack visual knowledge, i.e. they do not know much about…
Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
Piotr Padlewski, Max Bain, Matthew Henderson +19
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard diff…
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Reka Team, Aitor Ormazabal, Che Zheng +23
We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images,…
Latxa: An Open Language Model and Evaluation Suite for Basque
Julen Etxaniz, Oscar Sainz, Naiara Perez +6
We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque c…
Erato: Automatizing Poetry Evaluation
Manex Agirrezabal, Hugo Gonçalo Oliveira, Aitor Ormazabal
We present Erato, a framework designed to facilitate the automated evaluation of poetry, including that generated by poetry generation systems. Our framework employs a diverse set…
CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models
Aitor Ormazabal, Mikel Artetxe, Eneko Agirre
Methods for adapting language models (LMs) to new tasks and domains have traditionally assumed white-box access to the model, and work by modifying its parameters. However, this is…
Principled Paraphrase Generation with Parallel Corpora
Aitor Ormazabal, Mikel Artetxe, Aitor Soroa +2
Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision. In this paper, we formalize t…
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation
Aitor Ormazabal, Mikel Artetxe, Manex Agirrezabal +2
Formal verse poetry imposes strict constraints on the meter and rhyme scheme of poems. Most prior work on generating this type of poetry uses existing poems for supervision, which…
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring
Aitor Ormazabal, Mikel Artetxe, Aitor Soroa +2
Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embe…
Analyzing the Limitations of Cross-lingual Word Embedding Mappings
Aitor Ormazabal, Mikel Artetxe, Gorka Labaka +2
Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to…