Publications

Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects

Lixiao Zhu

AJung Moon

2024-03-11

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (publié)

Language-guided Skill Learning with Temporal Variational Inference

Haotian Fu

Pratyusha Sharma

Elias Stengel-Eskin

George Konidaris

Nicolas Le Roux

Marc-Alexandre Côté

Xingdi Yuan

We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose… (voir plus) an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. We test our system on BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment.Our results demonstrate that agents equipped with our method can discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks.

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

Perspectives on Robotic Systems for the Visually Impaired

Christopher Yee Wong

Rahatul Amin Ananto

Tanaka Akiyama

Joseph Paul Nemargut

AJung Moon

2024-03-11

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (publié)

Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science

Xiangru Tang

Qiao Jin

Kunlun Zhu

Tongxin Yuan

Yichi Zhang

Wangchunshu Zhou

Meng Qu

Yilun Zhao

Jian Tang

Zhuosheng Zhang

Arman Cohan

Zhiyong Lu

Mark Gerstein

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

Stealing Part of a Production Language Model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

David Rolnick

Florian Tramèr

2024-03-11

ArXiv (prépublication)

Stealing Part of a Production Language Model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dj Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

David Rolnick

Florian Tramèr

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like Op… (voir plus)enAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \

2024-03-11

ArXiv (prépublication)

Stealing Part of a Production Language Model

Nicholas Carlini

Daniel Paleka

Krishnamurthy Dj Dvijotham

Thomas Steinke

Jonathan Hayase

A. Feder Cooper

Katherine Lee

Matthew Jagielski

Milad Nasr

Arthur Conmy

Eric Wallace

David Rolnick

Florian Tramèr

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like Op… (voir plus)enAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \

2024-03-11

ArXiv (prépublication)

Stochastic gradient descent-based inference for dynamic network models with attractors

Hancong Pan

Xiaojing Zhu

Cantay Caliskan

Dino P. Christenson

Konstantinos Spiliopoulos

Dylan Walker

Eric Kolaczyk

2024-03-11

ArXiv (prépublication)

Stochastic gradient descent-based inference for dynamic network models with attractors

Hancong Pan

Xiaojing Zhu

Cantay Caliskan

Dino P. Christenson

Konstantinos Spiliopoulos

Dylan Walker

Eric Kolaczyk

In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate thei… (voir plus)r dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model to allow nodes to join and leave over time. Simulation results show that our extensions result in little loss of accuracy compared to MCMC, but can scale to much larger networks. We apply our approach to the longitudinal social networks of members of US Congress on the social media platform X. Accounting for node dynamics overcomes selection bias in the network and uncovers uniquely and increasingly repulsive forces within the Republican Party.

2024-03-11

ArXiv (prépublication)

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Xing Han Lu

Zdeněk Kasner

Siva Reddy

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve… (voir plus) real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github.io/weblinx

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Xing Han Lu

Zdeněk Kasner

Siva Reddy

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve… (voir plus) real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WebLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github.io/weblinx.

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)