68 citations · 120 across the 16 of their papers we have counts for
16 papers
Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity
Leon Engländer, Sophia Althammer, Ahmet Üstün +2
LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected information should naturally lead to a model e…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
Pat Verga, Sebastian Hofstatter, Sophia Althammer +6
As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe parti…
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection
Sophia Althammer, Guido Zuccon, Sebastian Hofstätter +2
Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning P…
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation
Mete Sertkan, Sophia Althammer, Sebastian Hofstätter
In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communiti…
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Sophia Althammer, Sebastian Hofstätter, Suzan Verberne +1
Robust test collections are crucial for Information Retrieval research. Recently there is a growing interest in evaluating retrieval systems for domain-specific retrieval tasks, ho…
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Sebastian Hofstätter, Omar Khattab, Sophia Althammer +2
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compa…
Establishing Strong Baselines for TripClick Health Retrieval
Sebastian Hofstätter, Sophia Althammer, Mete Sertkan +1
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection. We improve the - originally too…
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
Sophia Althammer, Sebastian Hofstätter, Mete Sertkan +2
Dense passage retrieval (DPR) models show great effectiveness gains in first stage retrieval for the web domain. However in the web domain we are in a setting with large amounts of…
A Time-Optimized Content Creation Workflow for Remote Teaching
Sebastian Hofstätter, Sophia Althammer, Mete Sertkan +1
We describe our workflow to create an engaging remote learning experience for a university course, while minimizing the post-production time of the educators. We make use of ubiqui…
Description-based Label Attention Classifier for Explainable ICD-9 Classification
Malte Feucht, Zhiliang Wu, Sophia Althammer +1
ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes. Auto…
DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
Sophia Althammer, Arian Askari, Suzan Verberne +1
In this paper, we present our approaches for the case law retrieval and the legal case entailment task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2021.…
Linguistically Informed Masking for Representation Learning in the Patent Domain
Sophia Althammer, Mark Buckley, Sebastian Hofstätter +1
Domain-specific contextualized language models have demonstrated substantial effectiveness gains for domain-specific downstream tasks, like similarity matching, entity recognition…
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility Study
Sophia Althammer, Sebastian Hofstätter, Allan Hanbury
Domain specific search has always been a challenging information retrieval task due to several challenges such as the domain specific language, the unique task setting, as well as…
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
Sebastian Hofstätter, Aldo Lipani, Sophia Althammer +2
Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear…
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
Sebastian Hofstätter, Sophia Althammer, Michael Schröder +2
Retrieval and ranking models are the backbone of many applications such as web search, open domain QA, or text-based recommender systems. The latency of neural ranking models at qu…