297 citations · 317 across the 13 of their papers we have counts for
6 papers · 1 filter
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability
Jiri Hron, Laura Culp, Gamaleldin Elsayed +28
While many capabilities of language models (LMs) improve with increased training budget, the influence of scale on hallucinations is not yet fully understood. Hallucinations come i…
Exploring and Benchmarking the Planning Capabilities of Large Language Models
Bernd Bohnet, Azade Nova, Aaron T Parisi +6
Classical and natural language planning tasks remain a difficult domain for modern large language models (LLMs). In this work, we lay the foundations for improving planning capabil…
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation
Bernd Bohnet, Kevin Swersky, Rosanne Liu +9
We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datase…
Many-Shot In-Context Learning
Rishabh Agarwal, Avi Singh, Lei M. Zhang +12
Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expande…
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…
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
Alon Jacovi, Yonatan Bitton, Bernd Bohnet +6
Prompting language models to provide step-by-step answers (e.g., "Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typ…