Kenneth Li

I am a fourth-year PhD student at Harvard, advised by Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. I am supported by Kempner Institute Graduate Fellowship. I hold a graduate-student Superalignment Fast Grant from OpenAI. I interned at MSR Asia and Meta AI.

I aim at understanding the inner workings of large language models and, based on these findings, control them for improved human-AI collaboration. Feel free to reach out if you'd like to discuss!

Contact: ke_li [at] g.harvard.edu
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Recent Research

	Dialogue Action Tokens: Steering Language Models in Goal-Directed Dialogue with a Multi-Turn Planner Kenneth Li, Yiming Wang, Fernanda Viégas, Martin Wattenberg preprint Arxiv \| Code A small planner model is trained using reinforcement learning to steer large language models in multi-round dialogues.
	Designing a Dashboard for Transparency and Control of Conversational AI Yida Chen, Aoyu Wu, Trevor DePodesta, Catherine Yeh, Kenneth Li, Nicholas Castillo Marin, Oam Patel, Jan Riecke, Shivam Raval, Olivia Seow, Fernanda Viégas, Martin Wattenberg preprint Arxiv \| Code \| Project Page We design and evaluate a dashboard interface for visualizing and controlling the internal user model in a conversational LLM.
	Measuring and Controlling Instruction (In)Stability in Language Model Dialogs Kenneth Li, Tianle Liu, Naomi Bashkansky, David Bau, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg COLM, 2024 (Oral) Arxiv \| Code \| The Gradient When a dialogue goes long, a chatbot ceases to follow its system prompt surprisingly quickly—within 8 rounds.
	Q-Probe: A Lightweight Approach to Reward Maximization for Language Models Kenneth Li, Samy Jelassi, Hugh Zhang, Sham Kakade, Martin Wattenberg, David Brandfonbrener ICML, 2024 Arxiv \| Code Through rejection sampling, we leverage a language model's own discriminative capability to boost its generative capability.
	Inference-Time Intervention: Eliciting Truthful Answers from a Language Model Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg NeurIPS, 2023 (Spotlight) Arxiv \| Code \| Stand-alone Model By manipulating the activations of a language model, we can compel it to tell the truth it knows but otherwise hides.
	Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task Kenneth Li, Aspen Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg ICLR, 2023 (Oral) Arxiv \| Code \| Demo \| The Gradient \| Scientific American \| The Atlantic \| Nature News \| Andrew Ng In a transformer trained on Othello transcripts, we uncover an interpretable and controllable world model of the game board.

Latest update: 08/2024