565 citations · 684 across the 23 of their papers we have counts for
18 papers · 1 filter
Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions
Anastasiia Sedova, Natalie Schluter, Skyler Seto +1
Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. When target language data is s…
How Value Induction Reshapes LLM Behaviour
Arnav Arora, Natalie Schluter, Katherine Metcalf +1
Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and empathy, and values, such as h…
Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks
Eileen Pan, Anna Seo Gyeong Choi, Maartje ter Hoeve +2
Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English diale…
Assessing the Role of Data Quality in Training Bilingual Language Models
Skyler Seto, Maartje ter Hoeve, Maureen de Seyssel +1
Bilingual and multilingual language models offer a promising path toward scaling NLP systems across diverse languages and users. However, their performance often varies wildly betw…
Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks
Maureen de Seyssel, Jie Chi, Skyler Seto +3
We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). I…
Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter
Verena Blaschke, Masha Fedzechkina, Maartje ter Hoeve
Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingu…
On the Way to LLM Personalization: Learning to Remember User Conversations
Lucie Charlotte Magister, Katherine Metcalf, Yizhe Zhang +1
Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to…
Training Bilingual LMs with Data Constraints in the Targeted Language
Skyler Seto, Maartje ter Hoeve, Richard He Bai +2
Large language models are trained on massive scrapes of the web, as required by current scaling laws. Most progress is made for English, given its abundance of high-quality pretrai…
GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning
Rita Ramos, Everlyn Asiko Chimoto, Maartje ter Hoeve +1
We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing…
High-Resource Methodological Bias in Low-Resource Investigations
Maartje ter Hoeve, David Grangier, Natalie Schluter
The central bottleneck for low-resource NLP is typically regarded to be the quantity of accessible data, overlooking the contribution of data quality. This is particularly seen in…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Julia Kiseleva, Alexey Skrynnik, Artem Zholus +14
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +18
Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Summarization with Graphical Elements
Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke
Automatic text summarization has experienced substantial progress in recent years. With this progress, the question has arisen whether the types of summaries that are typically gen…
Towards Interactive Language Modeling
Maartje ter Hoeve, Evgeny Kharitonov, Dieuwke Hupkes +1
Interaction between caregivers and children plays a critical role in human language acquisition and development. Given this observation, it is remarkable that explicit interaction…
Conversations with Documents. An Exploration of Document-Centered Assistance
Maartje ter Hoeve, Robert Sim, Elnaz Nouri +3
The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quick…
Understanding Multi-Head Attention in Abstractive Summarization
Joris Baan, Maartje ter Hoeve, Marlies van der Wees +2
Attention mechanisms in deep learning architectures have often been used as a means of transparency and, as such, to shed light on the inner workings of the architectures. Recently…
Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?
Joris Baan, Maartje ter Hoeve, Marlies van der Wees +2
Learning algorithms become more powerful, often at the cost of increased complexity. In response, the demand for algorithms to be transparent is growing. In NLP tasks, attention di…