Search Results for author:
Found 9 papers, 5 papers with code
MultiContrievers: Analysis of Dense Retrieval Representations
Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affects downstream tasks.
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis
We also find racial biases to be much more prevalent than gender biases.
This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models
Bias research in NLP seeks to analyse models for social biases, thus helping NLP practitioners uncover, measure, and mitigate social harms.
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages
To remedy this, we build a counterfactual evaluation corpus for gender and racial/migrant bias in four languages.
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations.
Intrinsic Bias Metrics Do Not Correlate with Application Bias
We urge researchers working on debiasing to focus on extrinsic measures of bias, and to make using these measures more feasible via creation of new challenge sets and annotated test data.
Scaling Systematic Literature Reviews with Machine Learning Pipelines
We construct a pipeline that automates each of these aspects, and experiment with many human-time vs. system quality trade-offs.
Content Planning for Neural Story Generation with Aristotelian Rescoring
Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion.
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation
We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50% improvement in story quality as compared to less interactive baselines.
