32 citations · 55 across the 11 of their papers we have counts for
11 papers · 1 filter
Language Models Struggle to Use Representations Learned In-Context
Michael A. Lepori, Tal Linzen, Ann Yuan +1
Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier goals of artificial intelligenc…
Dissecting Recall of Factual Associations in Auto-Regressive Language Models
Mor Geva, Jasmijn Bastings, Katja Filippova +1
Transformer-based language models (LMs) are known to capture factual knowledge in their parameters. While previous work looked into where factual associations are stored, only litt…
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
Irina Bejan, Artem Sokolov, Katja Filippova
Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art in NLP. However, data quality might have already become the bottleneck to unlock fu…
Understanding Text Classification Data and Models Using Aggregated Input Salience
Sebastian Ebert, Alice Shoshana Jakobovits, Katja Filippova
Realizing when a model is right for a wrong reason is not trivial and requires a significant effort by model developers. In some cases an input salience method, which highlights th…
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia +2
Feature attribution a.k.a. input salience methods which assign an importance score to a feature are abundant but may produce surprisingly different results for the same model on th…
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data
Katja Filippova
Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. To collect a large co…
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
Jasmijn Bastings, Katja Filippova
There is a recent surge of interest in using attention as explanation of model predictions, with mixed evidence on whether attention can be used as such. While attention convenient…
We Need to Talk About Random Splits
Anders Søgaard, Sebastian Ebert, Jasmijn Bastings +1
Gorman and Bedrick (2019) argued for using random splits rather than standard splits in NLP experiments. We argue that random splits, like standard splits, lead to overly optimisti…
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Katharina Kann, Sascha Rothe, Katja Filippova
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric fo…
Eval all, trust a few, do wrong to none: Comparing sentence generation models
Ondřej Cífka, Aliaksei Severyn, Enrique Alfonseca +1
In this paper, we study recent neural generative models for text generation related to variational autoencoders. Previous works have employed various techniques to control the prio…
Fast k-best Sentence Compression
Katja Filippova, Enrique Alfonseca
A popular approach to sentence compression is to formulate the task as a constrained optimization problem and solve it with integer linear programming (ILP) tools. Unfortunately, d…