activity
20192024
most citedGemini 1.5: Unlocking multimodal understanding across millions of tokens of context

297 citations · 583 across the 12 of their papers we have counts for

collaborators

17 papers

cs.CL2024★ 145 cited

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Morgane Riviere, Shreya Pathak +195

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…

cs.CL2024

From RAG to RICHES: Retrieval Interlaced with Sequence Generation

Palak Jain, Livio Baldini Soares, Tom Kwiatkowski

We present RICHES, a novel approach that interleaves retrieval with sequence generation tasks. RICHES offers an alternative to conventional RAG systems by eliminating the need for…

cs.CL2024★ 297 cited

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Petko Georgiev, Ving Ian Lei +1132

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…

cs.CL2023

1-PAGER: One Pass Answer Generation and Evidence Retrieval

Palak Jain, Livio Baldini Soares, Tom Kwiatkowski

We present 1-Pager the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. 1-Pager incrementally partitions the…

cs.CL2023

Calibrating Likelihoods towards Consistency in Summarization Models

Polina Zablotskaia, Misha Khalman, Rishabh Joshi +4

Despite the recent advances in abstractive text summarization, current summarization models still suffer from generating factually inconsistent summaries, reducing their utility fo…

cs.CL2023

NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders

Livio Baldini Soares, Daniel Gillick, Jeremy R. Cole +1

Neural document rerankers are extremely effective in terms of accuracy. However, the best models require dedicated hardware for serving, which is costly and often not feasible. To…