18 citations · 24 across the 5 of their papers we have counts for
8 papers
Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work
Rida Qadri, Remi Denton, Michael Madaio +13
Enterprise AI is transitioning from single-user, reactive tools toward proactive, multi-user 'teammates,' but our empirical understanding of this transition is limited. In this pap…
AI-Assisted Scientific Assessment: A Case Study on Climate Change
Christian Buck, Levke Caesar, Michelle Chen Huebscher +13
The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification, where agents explore search spaces in 'guess and check' loops. This paradigm do…
CLINB: A Climate Intelligence Benchmark for Foundational Models
Michelle Chen Huebscher, Katharine Mach, Aleksandar Stanić +10
Evaluating how Large Language Models (LLMs) handle complex, specialized knowledge remains a critical challenge. We address this through the lens of climate change by introducing CL…
Assessing Large Language Models on Climate Information
Jannis Bulian, Mike S. Schäfer, Afra Amini +8
As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grou…
Decoding a Neural Retriever's Latent Space for Query Suggestion
Leonard Adolphs, Michelle Chen Huebscher, Christian Buck +4
Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-…
Zero-Shot Retrieval with Search Agents and Hybrid Environments
Michelle Chen Huebscher, Christian Buck, Massimiliano Ciaramita +1
Learning to search is the task of building artificial agents that learn to autonomously use a search box to find information. So far, it has been shown that current language models…
Boosting Search Engines with Interactive Agents
Leonard Adolphs, Benjamin Boerschinger, Christian Buck +8
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses mach…
Meta Answering for Machine Reading
Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck +7
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.…