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Found 26 papers, 16 papers with code
Towards Geo-Culturally Grounded LLM Generations
Specifically, we compare the performance of standard LLMs, LLMs augmented with retrievals from a bespoke knowledge base (i. e., KB grounding), and LLMs augmented with retrievals from a web search (i. e., search grounding) on a series of cultural familiarity benchmarks.
MisgenderMender: A Community-Informed Approach to Interventions for Misgendering
We are the first to address this lack of research into interventions for misgendering by conducting a survey of gender-diverse individuals in the US to understand perspectives about automated interventions for text-based misgendering.
GeniL: A Multilingual Dataset on Generalizing Language
We argue that understanding the sentential context is crucial for detecting instances of generalization.
SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English.
MiTTenS: A Dataset for Evaluating Gender Mistranslation
Translation systems, including foundation models capable of translation, can produce errors that result in gender mistranslation, and such errors can be especially harmful.
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia.
SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata
The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions.
MISGENDERED: Limits of Large Language Models in Understanding Pronouns
Content Warning: This paper contains examples of misgendering and erasure that could be offensive and potentially triggering.
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Ranked #1 on
Question Answering
on StrategyQA
Cultural Re-contextualization of Fairness Research in Language Technologies in India
Recent research has revealed undesirable biases in NLP data and models.
Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN
Therefore, ML auditing tools must be (1) better aligned with ML4H auditing principles and (2) able to illuminate and characterize communities vulnerable to the most harm.
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks
How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given language model?
Re-contextualizing Fairness in NLP: The Case of India
In this paper, we focus on NLP fair-ness in the context of India.
DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation
Disinformation has become a serious problem on social media.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Coreference Resolution
on Winograd Schema Challenge
Representation Learning for Resource-Constrained Keyphrase Generation
State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data.
Socially Aware Bias Measurements for Hindi Language Representations
Language representations are efficient tools used across NLP applications, but they are strife with encoded societal biases.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models.
On Measures of Biases and Harms in NLP
Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability
High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining.
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks.
On Measuring and Mitigating Biased Inferences of Word Embeddings
Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them.
Attenuating Bias in Word Vectors
Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages.
Closed Form Word Embedding Alignment
We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e. g., GloVe or word2vec).
