mlsanityData to 7 Oct 2026

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

arXiv 2210.07700 ↗First posted 14 Oct 2022cs.CLcited by 80

Not on the map. Why?

Abstract (arXiv)

Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated discourse around the risks of societal harms they introduce, whether inadvertent or malicious. Several studies have explored these harms and called for their mitigation via development of safer, fairer models. Going beyond enumerating the risks of harms, this work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works' taxonomies of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators. Bridging diverse strands of research, this survey aims to serve as a practical guide for both LM researchers and practitioners, with explanations of different mitigation strategies' motivations, their limitations, and open problems for future research.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). Builds on 179 of its 273 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

179 of 273 references.

References · 179

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  9. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  10. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  11. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  12. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
38 more
  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  2. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2022Emergent Abilities of Large Language Modelscited by 2,483
  6. 2018Deep contextualized word representationscited by 2,285
  7. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  8. 2020Extracting Training Data from Large Language Modelscited by 2,144
  9. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
  10. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  11. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  12. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  13. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  14. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  15. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  16. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  17. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  18. 2018Hierarchical Neural Story Generationcited by 1,182
  19. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  20. 2022Red Teaming Language Models with Language Modelscited by 983
  21. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  22. 2022Mass-Editing Memory in a Transformercited by 976
  23. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  24. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  25. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  26. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
  27. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 793
  28. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  29. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
  30. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  31. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  32. 2020Aligning AI With Shared Human Valuescited by 706
  33. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  34. 2018Annotation Artifacts in Natural Language Inference Datacited by 656
  35. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  36. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
  37. 2019Defending Against Neural Fake Newscited by 553
  38. 2021Editing Factual Knowledge in Language Modelscited by 548

50 most cited of 179 All 179 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 23; 2024: 30; 2025: 18; 2026: 8.

80 papers.

Sort:

By area:

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  3. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 257
  4. 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 224
  5. 2022SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Controlcited by 191
  6. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  7. 2023Hallucinations in Large Multilingual Translation Modelscited by 126
  8. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  9. 2024The Art of Saying No: Contextual Noncompliance in Language Modelscited by 100
  10. 2023A Survey on Fairness in Large Language Modelscited by 91
  11. 2024Measuring Political Bias in Large Language Models: What Is Said and How It Is Saidcited by 89
  12. 2024Safeguarding Large Language Models: A Surveycited by 73
38 more
  1. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  2. 2023Contrasting Linguistic Patterns in Human and LLM-Generated News Textcited by 62
  3. 2024Do LLMs write like humans? Variation in grammatical and rhetorical stylescited by 62
  4. 2023Self-Detoxifying Language Models via Toxification Reversalcited by 45
  5. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  6. 2024Backtracking Improves Generation Safetycited by 31
  7. 2023Assessing Language Model Deployment with Risk Cardscited by 30
  8. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  9. 2024Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attackscited by 25
  10. 2025When One LLM Drools, Multi-LLM Collaboration Rulescited by 25
  11. 2023Zero-Shot Detection of Machine-Generated Codescited by 24
  12. 2024What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectioncited by 24
  13. 2023Global Voices, Local Biases: Socio-Cultural Prejudices across Languagescited by 23
  14. 2025Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systemscited by 22
  15. 2023LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Modelcited by 21
  16. 2024Language Models in Dialogue: Conversational Maxims for Human-AI Interactionscited by 21
  17. 2024Ethos: Rectifying Language Models in Orthogonal Parameter Spacecited by 20
  18. 2024Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classificationcited by 18
  19. 2024The 2024 Foundation Model Transparency Indexcited by 18
  20. 2023Fairness in Language Models Beyond English: Gaps and Challengescited by 16
  21. 2024Teaching LLMs to Abstain across Languages via Multilingual Feedbackcited by 14
  22. 2024Identifying and Mitigating Social Bias Knowledge in Language Modelscited by 13
  23. 2023A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applicationscited by 12
  24. 2024Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approachcited by 12
  25. 2023Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societiescited by 11
  26. 2024RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Frameworkcited by 11
  27. 2023Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Textcited by 10
  28. 2023Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Modelscited by 10
  29. 2024Cultural Value Differences of LLMs: Prompt, Language, and Model Sizecited by 10
  30. 2023Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMscited by 9
  31. 2025The 2025 Foundation Model Transparency Indexcited by 9
  32. 2024Mitigating Social Bias in Large Language Models: A Multi-Objective Approach within a Multi-Agent Frameworkcited by 8
  33. 2025SAGE: A Generic Framework for LLM Safety Evaluationcited by 7
  34. 2025Steering Towards Fairness: Mitigating Political Bias in LLMscited by 7
  35. 2024Biased AI can Influence Political Decision-Makingcited by 6
  36. 2025Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimizationcited by 6
  37. 2025Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMscited by 6
  38. 2024Measuring Human Contribution in AI-Assisted Content Generationcited by 5

50 most cited of 80 All 80 (JSON)