Search Results for author:
Found 18 papers, 6 papers with code
Investigating Language Relationships in Multilingual Sentence Encoders Through the Lens of Linguistic Typology
The results provide insight into their information-sharing mechanisms and suggest that these linguistic properties are encoded jointly across typologically similar languages in these models.
Local Contrastive Editing of Gender Stereotypes
Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology, yet our understanding of how bias manifests in the parameters of LMs remains incomplete.
Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning
Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic.
On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?
While multilingual language models (MLMs) have been trained on 100+ languages, they are typically only evaluated across a handful of them due to a lack of available test data in most languages.
The Echoes of Multilinguality: Tracing Cultural Value Shifts during LM Fine-tuning
Texts written in different languages reflect different culturally-dependent beliefs of their writers.
Metaphor Understanding Challenge Dataset for LLMs
Metaphor understanding is therefore an essential task for large language models (LLMs).
Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks
Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing.
Do large language models solve verbal analogies like children do?
We conclude that the LLMs we tested indeed tend to solve verbal analogies by association with C like children do.
Probing LLMs for Joint Encoding of Linguistic Categories
Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining.
How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning
We further study how different fine-tuning languages influence model performance on a given test language and find that they can both reinforce and complement the knowledge acquired from data of the test language itself.
Data-Efficient Cross-Lingual Transfer with Language-Specific Subnetworks
Large multilingual language models typically share their parameters across all languages, which enables cross-lingual task transfer, but learning can also be hindered when training updates from different languages are in conflict.
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?
In this paper, we investigate what types of stereotypical information are captured by pretrained language models.
Cross-neutralising: Probing for joint encoding of linguistic information in multilingual models
Multilingual sentence encoders are widely used to transfer NLP models across languages.
What does it mean to be language-agnostic? Probing multilingual sentence encoders for typological properties
Multilingual sentence encoders have seen much success in cross-lingual model transfer for downstream NLP tasks.
Blackbox Meets Blackbox: Representational Similarity \& Stability Analysis of Neural Language Models and Brains
In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models.
Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains
In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models.
Semantic Drift in Multilingual Representations
We propose to conduct an adapted version of representational similarity analysis of a selected set of concepts in computational multilingual representations.
Robust Evaluation of Language-Brain Encoding Experiments
Language-brain encoding experiments evaluate the ability of language models to predict brain responses elicited by language stimuli.
