most citedComparative Knowledge Distillation

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.AI2024

Tree Search for Language Model Agents

Jing Yu Koh, Stephen McAleer, Daniel Fried +1

Autonomous agents powered by language models (LMs) have demonstrated promise in their ability to perform decision-making tasks such as web automation. However, a key limitation rem…

cs.LG2024

Dissecting Adversarial Robustness of Multimodal LM Agents

Chen Henry Wu, Rishi Shah, Jing Yu Koh +3

As language models (LMs) are used to build autonomous agents in real environments, ensuring their adversarial robustness becomes a critical challenge. Unlike chatbots, agents are c…

cs.CL2024

Repetition Improves Language Model Embeddings

Jacob Mitchell Springer, Suhas Kotha, Daniel Fried +2

Bidirectional models are considered essential for strong text embeddings. Recent approaches to adapt autoregressive language models (LMs) into strong text embedding models have lar…

cs.LG2024

VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks

Jing Yu Koh, Robert Lo, Lawrence Jang +7

Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchma…

cs.LG20231 cited

Comparative Knowledge Distillation

Alex Wilf, Alex Tianyi Xu, Paul Pu Liang +3

In the era of large scale pretrained models, Knowledge Distillation (KD) serves an important role in transferring the wisdom of computationally heavy teacher models to lightweight,…

cs.AI2023

SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

Xuhui Zhou, Hao Zhu, Leena Mathur +8

Humans are social beings; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusiv…