Pushmeet Kohli


pdf bib
Challenges in Detoxifying Language Models
Johannes Welbl | Amelia Glaese | Jonathan Uesato | Sumanth Dathathri | John Mellor | Lisa Anne Hendricks | Kirsty Anderson | Pushmeet Kohli | Ben Coppin | Po-Sen Huang
Findings of the Association for Computational Linguistics: EMNLP 2021

Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of safety is imperative for deploying LMs in the real world; to this end, prior work often relies on automatic evaluation of LM toxicity. We critically discuss this approach, evaluate several toxicity mitigation strategies with respect to both automatic and human evaluation, and analyze consequences of toxicity mitigation in terms of model bias and LM quality. We demonstrate that while basic intervention strategies can effectively optimize previously established automatic metrics on the REALTOXICITYPROMPTS dataset, this comes at the cost of reduced LM coverage for both texts about, and dialects of, marginalized groups. Additionally, we find that human raters often disagree with high automatic toxicity scores after strong toxicity reduction interventions—highlighting further the nuances involved in careful evaluation of LM toxicity.


pdf bib
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Po-Sen Huang | Huan Zhang | Ray Jiang | Robert Stanforth | Johannes Welbl | Jack Rae | Vishal Maini | Dani Yogatama | Pushmeet Kohli
Findings of the Association for Computational Linguistics: EMNLP 2020

Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of generating coherent texts, it also prompts models to internalize social biases present in the training corpus. This paper aims to quantify and reduce a particular type of bias exhibited by language models: bias in the sentiment of generated text. Given a conditioning context (e.g., a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g., country names, occupations, genders) in the conditioning context using a form of counterfactual evaluation. We quantify sentiment bias by adopting individual and group fairness metrics from the fair machine learning literature, and demonstrate that large-scale models trained on two different corpora (news articles, and Wikipedia) exhibit considerable levels of bias. We then propose embedding and sentiment prediction-derived regularization on the language model’s latent representations. The regularizations improve fairness metrics while retaining comparable levels of perplexity and semantic similarity.


pdf bib
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Po-Sen Huang | Robert Stanforth | Johannes Welbl | Chris Dyer | Dani Yogatama | Sven Gowal | Krishnamurthy Dvijotham | Pushmeet Kohli
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and data augmentation to partially mitigate such brittleness, but these are unlikely to find worst-case adversaries due to the complexity of the search space arising from discrete text perturbations. In this work, we approach the problem from the opposite direction: to formally verify a system’s robustness against a predefined class of adversarial attacks. We study text classification under synonym replacements or character flip perturbations. We propose modeling these input perturbations as a simplex and then using Interval Bound Propagation – a formal model verification method. We modify the conventional log-likelihood training objective to train models that can be efficiently verified, which would otherwise come with exponential search complexity. The resulting models show only little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries.


pdf bib
Story Cloze Evaluator: Vector Space Representation Evaluation by Predicting What Happens Next
Nasrin Mostafazadeh | Lucy Vanderwende | Wen-tau Yih | Pushmeet Kohli | James Allen
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

pdf bib
A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
Nasrin Mostafazadeh | Nathanael Chambers | Xiaodong He | Devi Parikh | Dhruv Batra | Lucy Vanderwende | Pushmeet Kohli | James Allen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Visual Storytelling
Ting-Hao Kenneth Huang | Francis Ferraro | Nasrin Mostafazadeh | Ishan Misra | Aishwarya Agrawal | Jacob Devlin | Ross Girshick | Xiaodong He | Pushmeet Kohli | Dhruv Batra | C. Lawrence Zitnick | Devi Parikh | Lucy Vanderwende | Michel Galley | Margaret Mitchell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies