mlsanityData to 7 Oct 2026

The Call for Socially Aware Language Technologies

arXiv 2405.02411 ↗First posted 3 May 2024cs.CLcited by 12

Not on the map. Why?

Abstract (arXiv)

Language technologies have made enormous progress, especially with the introduction of large language models (LLMs). On traditional tasks such as machine translation and sentiment analysis, these models perform at near-human level. These advances can, however, exacerbate a variety of issues that models have traditionally struggled with, such as bias, evaluation, and risks. In this position paper, we argue that many of these issues share a common core: a lack of awareness of the factors, context, and implications of the social environment in which NLP operates, which we call social awareness. While NLP is getting better at solving the formal linguistic aspects, limited progress has been made in adding the social awareness required for language applications to work in all situations for all users. Integrating social awareness into NLP models will make applications more natural, helpful, and safe, and will open up new possibilities. Thus we argue that substantial challenges remain for NLP to develop social awareness and that we are just at the beginning of a new era for the field.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 36 of its 88 references that are in the map. First posted on arXiv 3 May 2024.

Builds on

36 of 88 references.

References · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  3. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  4. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  5. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  6. 2023Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructionscited by 377
  7. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  8. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 221
  9. 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
  10. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 216
  11. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  12. 2024A Roadmap to Pluralistic Alignmentcited by 175
24 more
  1. 2023Understanding Social Reasoning in Language Models with Language Modelscited by 166
  2. 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
  3. 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
  4. 2021ArtEmis: Affective Language for Visual Artcited by 103
  5. 2019Getting Gender Right in Neural Machine Translationcited by 98
  6. 2023Identifying and Mitigating the Security Risks of Generative AIcited by 90
  7. 2021On Measures of Biases and Harms in NLPcited by 81
  8. 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
  9. 2021Including Signed Languages in Natural Language Processingcited by 67
  10. 2024Unintended Impacts of LLM Alignment on Global Representationcited by 66
  11. 2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 62
  12. 2022Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP Worldcited by 55
  13. 2023Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakescited by 51
  14. 2020EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principlecited by 50
  15. 2023You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instrumentscited by 48
  16. 2015Computational Sociolinguistics: A Surveycited by 35
  17. 2023Mirages: On Anthropomorphism in Dialogue Systemscited by 30
  18. 2023A Diachronic Perspective on User Trust in AI under Uncertaintycited by 23
  19. 2023What's the Meaning of Superhuman Performance in Today's NLU?cited by 22
  20. 2019Toward Fairness in AI for People with Disabilities: A Research Roadmapcited by 21
  21. 2023A Material Lens on Coloniality in NLPcited by 19
  22. 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impactcited by 18
  23. 2022SocioProbe: What, When, and Where Language Models Learn about Sociodemographicscited by 7
  24. 2021More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networkscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 3; 2026: 2.

12 papers.

  1. 2024Can LLM be a Personalized Judge?cited by 99
  2. 2024The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Modelscited by 19
  3. 2024The Sociolinguistic Foundations of Language Modelingcited by 13
  4. 2025SocialGesture: Delving into Multi-person Gesture Understandingcited by 11
  5. 2025AI Awarenesscited by 6
  6. 2025What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigmacited by 5
  7. 2024Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarcecited by 4
  8. 2024ExpressivityBench: Can LLMs Communicate Implicitly?cited by 2
  9. 2026The Hidden Puppet Master: Predicting Human Belief Change in Manipulative LLM Dialoguescited by 2
  10. 2024Toward Cultural Interpretability: A Linguistic Anthropological Framework for Describing and Evaluating Large Language Models (LLMs)cited by 1
  11. 2024Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerptscited by 0
  12. 2026Who Are They to Each Other? Multi-Agent Reasoning for Speaker Relationship Inferencecited by 0