3 papers
cs.LG2025
DataDecide: How to Predict Best Pretraining Data with Small Experiments
Ian Magnusson, Nguyen Tai, Ben Bogin +10
Because large language models are expensive to pretrain on different datasets, using smaller-scale experiments to decide on data is crucial for reducing costs. Which benchmarks and…
cs.CL2025
Leveraging In-Context Learning for Language Model Agents
Shivanshu Gupta, Sameer Singh, Ashish Sabharwal +2
In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to…
cs.CL2024
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?
Ori Yoran, Samuel Joseph Amouyal, Chaitanya Malaviya +3
Language agents, built on top of language models (LMs), are systems that can interact with complex environments, such as the open web. In this work, we examine whether such agents…