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Found 7 papers, 4 papers with code
Can We Use Small Models to Investigate Multimodal Fusion Methods?
This is a setting in which we fuse language with information from the math modality and strive to replicate some fusion methods from the vision-and-language domain.
The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models
In this work, we identify potential causes of inconsistency and evaluate the effectiveness of two mitigation strategies: up-scaling and augmenting the LM with a retrieval corpus.
Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models
Inspired by this, we replace the semantic retrieval in Retro with a surface-level method based on BM25, obtaining a significant reduction in perplexity.
On the Generalization Ability of Retrieval-Enhanced Transformers
Recent work on the Retrieval-Enhanced Transformer (RETRO) model has shown that off-loading memory from trainable weights to a retrieval database can significantly improve language modeling and match the performance of non-retrieval models that are an order of magnitude larger in size.
Transferring Knowledge from Vision to Language: How to Achieve it and how to Measure it?
We find that our method can successfully be used to measure visual knowledge transfer capabilities in models and that our novel model architecture shows promising results for leveraging multimodal knowledge in a unimodal setting.
Building a Swedish Open-Domain Conversational Language Model
We present on-going work of evaluating the, to our knowledge, first large generative language model trained to converse in Swedish, using data from the online discussion forum Flashback.
