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Mitigating Societal Harms in Large Language Models

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WorkDifferentWithAI.com Academic Paper Alert!

Written by Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, Yulia Tsvetkov, Qi Zhang, Hassan Sajjad

Category: Ethical AI

Article Section: Ethical and Responsible AI; Responsible AI Practices

Publication Date: 2023-12

SEO Description: “Explore strategies for reducing societal harms in AI language models at EMNLP 2023 tutorial.”

Keywords

societal harms, language models, social biases, misinformation, privacy violations

AI-Generated Paper Summary

Generated by Ethical AI Researcher GPT

The paper “Mitigating Societal Harms in Large Language Models” by Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov presents a comprehensive survey and typology of approaches to mitigate societal harms caused by language models. This work is critical in the context of increasing deployment of natural language processing (NLP) technologies in various user-facing products. The authors focus on identifying and addressing various risks such as toxicity, bias, misinformation, factual inconsistency, and privacy violations in language generation models. They propose systematic methods to identify and eliminate risks at different stages of model development, including data collection, model training, and language generation. This tutorial-style paper aims to equip NLP researchers and engineers with practical tools for mitigating safety risks associated with pre-trained language generation models.

Author Caliber:

  • Sachin Kumar: Ph.D. candidate at Carnegie Mellon University, focusing on core language generation with deep learning.
  • Vidhisha Balachandran: Ph.D. student at Carnegie Mellon University, with a focus on building interpretable and reliable NLP models.
  • Lucille Njoo: Ph.D. student at the University of Washington, working at the intersection of NLP, ethics, and computational social science.
  • Antonios Anastasopoulos: Assistant Professor at George Mason University, specializing in NLP for local and low-resource languages.
  • Yulia Tsvetkov: Assistant Professor at the University of Washington, focusing on computational ethics, multilingual NLP, and machine learning for NLP.

Novelty & Merit:

  1. Unified typology of technical approaches for mitigating language generation model harms.
  2. Comprehensive overview of potential social issues in language generation.
  3. Systematic identification and mitigation strategies at various stages of model development.
  4. Practical tools for safety risk mitigation in pre-trained language models.

Commercial Applications:

  1. Development of safer and more ethical language generation models for various applications, including dialogue systems, recommendation systems, and machine translation.
  2. Implementation of these methods in NLP-based products to ensure compliance with ethical standards and reduce legal and reputational risks.
  3. Use in educational and research settings to guide future developments in ethical AI and NLP.

Findings and Conclusions:

  1. Language models can cause societal harms such as toxicity, bias, and misinformation.
  2. Risks can be systematically identified and mitigated at different stages of model development.
  3. A unified taxonomy of mitigation strategies can guide researchers and practitioners in developing safer language models.
  4. Practical tools and methods are available for mitigating safety risks in pre-trained language models.

Author’s Abstract

Numerous recent studies have highlighted societal harms that can be caused by language technologies deployed in the wild. While several surveys, tutorials, and workshops have discussed the risks of harms in specific contexts – e.g., detecting and mitigating gender bias in NLP models – no prior work has developed a unified typology of technical approaches for mitigating harms of language generation models. Our tutorial is based on a survey we recently wrote that proposes such a typology. We will provide an overview of potential social issues in language generation, including toxicity, social biases, misinformation, factual inconsistency, and privacy violations. Our primary focus will be on how to systematically identify risks, and how eliminate them at various stages of model development, from data collection, to model development, to inference/language generation. Through this tutorial, we aim to equip NLP researchers and engineers with a suite of practical tools for mitigating safety risks from pretrained language generation models.

Read the full paper here

Last updated on December 11th, 2023.