132 citations · 320 across the 15 of their papers we have counts for
14 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…
In-Context Probing: Toward Building Robust Classifiers via Probing Large Language Models
Afra Amini, Massimiliano Ciaramita
Large language models are able to learn new tasks in context, where they are provided with instructions and a few annotated examples. However, the effectiveness of in-context learn…
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Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice.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…
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian +2
We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilit…
ClimaText: A Dataset for Climate Change Topic Detection
Francesco S. Varini, Jordan Boyd-Graber, Massimiliano Ciaramita +1
Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an impo…
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.…
On Identifiability in Transformers
Gino Brunner, Yang Liu, Damián Pascual +3
In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the ident…
Zero-Shot Dual Machine Translation
Lierni Sestorain, Massimiliano Ciaramita, Christian Buck +1
Neural Machine Translation (NMT) systems rely on large amounts of parallel data. This is a major challenge for low-resource languages. Building on recent work on unsupervised and s…
Analyzing Language Learned by an Active Question Answering Agent
Christian Buck, Jannis Bulian, Massimiliano Ciaramita +4
We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed…
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita +4
We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black bo…
Explaining away ambiguity: Learning verb selectional preference with Bayesian networks
Massimiliano Ciaramita, Mark Johnson
This paper presents a Bayesian model for unsupervised learning of verb selectional preferences. For each verb the model creates a Bayesian network whose architecture is determined…