Maarten Sap

I am an assistant professor at CMU's LTI department, and a part-time research scientist at the Allen Institute for AI (AI2). My research focuses on endowing NLP systems with social intelligence and social commonsense, and understanding social inequality and bias in language.

Before this, I was a Postdoc/Young Investigator at the Allen Institute for AI (AI2), working on project Mosaic. I received my PhD from the University of Washington where I was advised by Noah Smith and Yejin Choi, and have interned at AI2 working on social commonsense reasoning, and at Microsoft Research working on deep learning models for understanding human cognition.
[bio for talks]

Recent updates:

January 2024 πŸ‘¨πŸΌβ€πŸ«βœ¨: I've been prepping and polishing slides for my class 11-830 Ethics, Social Biases, and Positive Impact in Language Technologies which I'll be teaching alone this semester!

December 2023 πŸ’¬πŸ†: Super excited that we won Outstanding Paper Award at EMNLP 2023 for our paper SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization!!!!

November 2023 πŸ“°πŸ¦: Excited to unveil the camera-ready versions of our EMNLP papers! (1) "Don't Take This Out of Context!" On the Need for Contextual Models and Evaluations for Stylistic Rewriting, (2) SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization, (3) FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions, (4) Modeling Empathic Similarity in Personal Narratives, (5) BiasX: "Thinking Slow" in Toxic Language Annotation with Explanations of Implied Social Biases, and (6) Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language.

August 2023 πŸ‘¨πŸΌβ€πŸ«: Year two of being a professor has started! I'm excited about this coming year, and teaching the Data Science Seminar!

August 2023 πŸŽΆπŸ—½: I was invited to give a remote talk about The Pivotal Role of Social Context in Toxic Language Detection to Spotify's Ethical AI team!

July 2023 πŸ’»πŸŒΊ: Excited to give a (virtual) keynote talk at the first Workshop on Theory of Mind at ICML 2023: Towards Socially Aware AI with Pragmatic Competence

July 2023 πŸ³οΈβ€πŸŒˆπŸ†: Extremely excited to share that we won an Outstanding Paper award for our ACL 2023 paper NLPositionality: Characterizing Design Biases of Datasets and Models with Sebastin, Jenny, Ronan, and Katharina!

[older news]


My research group:

Joel Mire

LTI MLT student

Karina Halevy

LTI PhD student
co-advised with Mona Diab

Jimin Mun

LTI PhD student

Jocelyn Shen

MIT PhD student
co-advised with Cynthia Breazeal

Akhila Yerukola

LTI PhD student

Xuhui Zhou

LTI PhD student


Overarching Research Themes

Detecting and Mitigating Social Biases in Language

Language can perpetuate social biases and toxicity against oppressed or marginalized groups. I want to investigate new ways of representing and detecting such harmful content in text (e.g., Social Bias Frames). Additionally, I want to harness NLP systems to combat stereotypical or harmful statements in language, through controllable text generation (e.g., with DExperts) or controllable text debiasing (e.g., with PowerTransformer).

In the future, I want to make this technology more context-aware and human-centric, e.g., by incorporating power differentials between speaker and listener, and studying human-in-the-loop methods for toxicity detection or text debiasing.

Commonsense Reasoning for Socially Aware NLP

Through theory-of-mind, Humans are trivially able to reason about other people's intents and reactions to everyday situations. I am interested in studying how AI systems can do this type of social commonsense reasoning. For example, this requires giving models knowledge of social commensense (e.g., with Event2Mind or ATOMIC, and methods like CoMET) or social acceptibility (Social Chemistry). Additionally, this requires creating benchmarks for measuring models' social commonsense abilities (e.g., with Social IQa, or Story Commonsense).

In the future, I want to keep investigating this elusive goal of machine social commonsense, which we show is still not present in LLMs like GPT-3. Additionally, I want to explore positive applications of this research, e.g., for therapeutic setting or for helping people with cognitive disabilities.

Analyzing the Ethics and Transparency of AI models

AI and NLP systems unfortunately encode social biases and stereotypes. I'm passionate about analyzing and diagnosing the potential negative societal impacts of these systems. For example, I've uncovered severe racial bias in hate speech detection datasets and models, and subsequently analyzed whether robustness methods for NLP can mitigate them, as well as understanding the psychological attitudes that cause over- and under-detection of content as toxic. Additionally, I've scrutinized recent pretrained language models and their training data with respect to biases, toxicity, and fake news (e.g., measuring GPT-2 and GPT-3's neural toxic degeneration, and documenting the English C4 Webtext Crawl).

In the future, I plan to keep diagnosing and mitigating the ethical, fairness, and representation issues in AI systems, especially from a human-centric perspective of end-users and other stakeholders.

Socially Aware Conversational AI

Conversational agents are one of the most important ways that humans will interface with AI systems, whether they are talk with AI systems (e.g., chatbots) or chatting with each others with AI-assisted interfaces (e.g., smart suggestions). I'm interested in studying how we can make conversational agents more socially aware and better respond to toxicity, based on my experience winning the inaugural Alexa Prize in 2017. For example, in our ToxiChat paper, we quantified how chatbots often agree with toxic utterances more than with neutral comments. I've also developed large-scale datasets for developing dialogue agents that are more prosocial (ProSocial Dialogs) and socially competent (with SODA).

In the future, I am interested in understanding how conversational agents can better reason about communicative intent when responding, how communication can be learned in an emergent way, and how we can use reinforcement learning to make dialog agents better.