62 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of CambridgeGB6 papers
- Tallinn University of TechnologyEE5 papers
- University of EdinburghGB5 papers
- University of AmsterdamNL4 papers
- Carnegie Mellon UniversityUS2 papers
- Tsinghua UniversityCN2 papers
- University of ChicagoUS2 papers
- Alibaba Group (United States)US1 paper
- Allen InstituteUS1 paper
- Amazon (United States)US1 paper
- Argonne National LaboratoryUS1 paper
- Artificial Intelligence in Medicine (Canada)CA1 paper
6 papers · 1 filter
Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)
Francesca Padovani, Bastian Bunzeck, Manar Ali +4
We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we empl…
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework
Jiaqi Weng, Han Zheng, Hanyu Zhang +6
Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, under what circumstances SAEs derive mos…
TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs
Ezgi Başar, Francesca Padovani, Jaap Jumelet +1
We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs).…
Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
Francesca Padovani, Jaap Jumelet, Yevgen Matusevych +1
Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can reach similar syntactic abilities as LMs trained on muc…
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
Leonardo Ranaldi, Barry Haddow, Alexandra Birch
Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through…
On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
Jirui Qi, Raquel Fernández, Arianna Bisazza
Retrieval-augmented generation (RAG) with large language models (LLMs) has demonstrated strong performance in multilingual question-answering (QA) tasks by leveraging relevant pass…