13 research outputs found
Erasure of Unaligned Attributes from Neural Representations
We present the Assignment-Maximization Spectral Attribute removaL (AMSAL)
algorithm, which erases information from neural representations when the
information to be erased is implicit rather than directly being aligned to each
input example. Our algorithm works by alternating between two steps. In one, it
finds an assignment of the input representations to the information to be
erased, and in the other, it creates projections of both the input
representations and the information to be erased into a joint latent space. We
test our algorithm on an extensive array of datasets, including a Twitter
dataset with multiple guarded attributes, the BiasBios dataset and the
BiasBench benchmark. The last benchmark includes four datasets with various
types of protected attributes. Our results demonstrate that bias can often be
removed in our setup. We also discuss the limitations of our approach when
there is a strong entanglement between the main task and the information to be
erased.Comment: Accepted to Transactions of the Association for Computational
Linguistics, 22 pages (pre-MIT Press publication version
A Joint Matrix Factorization Analysis of Multilingual Representations
We present an analysis tool based on joint matrix factorization for comparing
latent representations of multilingual and monolingual models. An alternative
to probing, this tool allows us to analyze multiple sets of representations in
a joint manner. Using this tool, we study to what extent and how
morphosyntactic features are reflected in the representations learned by
multilingual pre-trained models. We conduct a large-scale empirical study of
over 33 languages and 17 morphosyntactic categories. Our findings demonstrate
variations in the encoding of morphosyntactic information across upper and
lower layers, with category-specific differences influenced by language
properties. Hierarchical clustering of the factorization outputs yields a tree
structure that is related to phylogenetic trees manually crafted by linguists.
Moreover, we find the factorization outputs exhibit strong associations with
performance observed across different cross-lingual tasks. We release our code
to facilitate future research.Comment: Accepted to Findings of EMNLP 202
Erasure of Unaligned Attributes from Neural Representations
AbstractWe present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example. Our algorithm works by alternating between two steps. In one, it finds an assignment of the input representations to the information to be erased, and in the other, it creates projections of both the input representations and the information to be erased into a joint latent space. We test our algorithm on an extensive array of datasets, including a Twitter dataset with multiple guarded attributes, the BiasBios dataset, and the BiasBench benchmark. The latter benchmark includes four datasets with various types of protected attributes. Our results demonstrate that bias can often be removed in our setup. We also discuss the limitations of our approach when there is a strong entanglement between the main task and the information to be erased.
Detecting and Mitigating Hallucinations in Multilingual Summarisation
Hallucinations pose a significant challenge to the reliability of neural
models for abstractive summarisation. While automatically generated summaries
may be fluent, they often lack faithfulness to the original document. This
issue becomes even more pronounced in low-resource settings, such as
cross-lingual transfer. With the existing faithful metrics focusing on English,
even measuring the extent of this phenomenon in cross-lingual settings is hard.
To address this, we first develop a novel metric, mFACT, evaluating the
faithfulness of non-English summaries, leveraging translation-based transfer
from multiple English faithfulness metrics. We then propose a simple but
effective method to reduce hallucinations with a cross-lingual transfer, which
weighs the loss of each training example by its faithfulness score. Through
extensive experiments in multiple languages, we demonstrate that mFACT is the
metric that is most suited to detect hallucinations. Moreover, we find that our
proposed loss weighting method drastically increases both performance and
faithfulness according to both automatic and human evaluation when compared to
strong baselines for cross-lingual transfer such as MAD-X. Our code and dataset
are available at https://github.com/yfqiu-nlp/mfact-summ
Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information
We describe a simple and effective method (Spectral Attribute removaL; SAL)
to remove private or guarded information from neural representations. Our
method uses matrix decomposition to project the input representations into
directions with reduced covariance with the guarded information rather than
maximal covariance as factorization methods normally use. We begin with linear
information removal and proceed to generalize our algorithm to the case of
nonlinear information removal using kernels. Our experiments demonstrate that
our algorithm retains better main task performance after removing the guarded
information compared to previous work. In addition, our experiments demonstrate
that we need a relatively small amount of guarded attribute data to remove
information about these attributes, which lowers the exposure to sensitive data
and is more suitable for low-resource scenarios. Code is available at
https://github.com/jasonshaoshun/SAL.Comment: Accepted to the Conference of the European Chapter of the Association
for Computational Linguistics (EACL), 2023; 12 page
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents by Sampling Summary Views
We argue that disentangling content selection from the budget used to cover
salient content improves the performance and applicability of abstractive
summarizers. Our method, FactorSum, does this disentanglement by factorizing
summarization into two steps through an energy function: (1) generation of
abstractive summary views; (2) combination of these views into a final summary,
following a budget and content guidance. This guidance may come from different
sources, including from an advisor model such as BART or BigBird, or in oracle
mode -- from the reference. This factorization achieves significantly higher
ROUGE scores on multiple benchmarks for long document summarization, namely
PubMed, arXiv, and GovReport. Most notably, our model is effective for domain
adaptation. When trained only on PubMed samples, it achieves a 46.29 ROUGE-1
score on arXiv, which indicates a strong performance due to more flexible
budget adaptation and content selection less dependent on domain-specific
textual structure