Search Results for author: Christian Buck

Found 22 papers, 4 papers with code

Assessing Large Language Models on Climate Information

no code implementations4 Oct 2023 Jannis Bulian, Mike S. Schäfer, Afra Amini, Heidi Lam, Massimiliano Ciaramita, Ben Gaiarin, Michelle Chen Hübscher, Christian Buck, Niels G. Mede, Markus Leippold, Nadine Strauß

As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains.

Zero-Shot Retrieval with Search Agents and Hybrid Environments

no code implementations30 Sep 2022 Michelle Chen Huebscher, Christian Buck, Massimiliano Ciaramita, Sascha Rothe

We extend the previous learning to search setup to a hybrid environment, which accepts discrete query refinement operations, after a first-pass retrieval step via a dual encoder.


Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation

1 code implementation15 Feb 2022 Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Boerschinger, Tal Schuster

The predictions of question answering (QA)systems are typically evaluated against manually annotated finite sets of one or more answers.

Question Answering

Meta Answering for Machine Reading

no code implementations11 Nov 2019 Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, Lierni Sestorain Saralegu

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.

Natural Questions Question Answering +1

Zero-Shot Dual Machine Translation

1 code implementation25 May 2018 Lierni Sestorain, Massimiliano Ciaramita, Christian Buck, Thomas Hofmann

Our method can obtain improvements also on the setting where a small amount of parallel data for the zero-shot language pair is available.

Machine Translation NMT +1

Analyzing Language Learned by an Active Question Answering Agent

no code implementations23 Jan 2018 Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang

We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017].

Information Retrieval Question Answering +3

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

2 code implementations ICLR 2018 Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang

The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.

Information Retrieval Question Answering +3

N-gram Counts and Language Models from the Common Crawl

no code implementations LREC 2014 Christian Buck, Kenneth Heafield, Bas van Ooyen

We contribute 5-gram counts and language models trained on the Common Crawl corpus, a collection over 9 billion web pages.

Language Modelling Machine Translation +1

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