Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference.

LMs a visual sketchpad and tools to draw on the sketchpad.

This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.

Recently, Language Models (LMs) have demonstrated complex reasoning capacities even without any finetuning.

Integrated development environments (IDEs) assist developers in understanding repository context using static analysis.

Wethen comprehensively study detoxifying LMswithparameter sizesranging from126Mupto530B(3 largerthanGPT3), a scale that has never been studied before. We find thati) large LMs have similar toxicity levels as smaller ones given the same pre-training corpus, and ii) large LMs require more endeavor to unlearn the toxic content seen at pretraining. Wealso explore parameter-efficient training methods fordetoxification.