18 citations · 23 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice.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.…