14.5k citations · 22.4k across the 46 of their papers we have counts for
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TapeAgents: a Holistic Framework for Agent Development and Optimization
Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang +10
We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents…
NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild
Shikhar Murty, Hao Zhu, Dzmitry Bahdanau +1
We introduce NNetNav, a method for unsupervised interaction with websites that generates synthetic demonstrations for training browser agents. Given any website, NNetNav produces t…
LLMs can learn self-restraint through iterative self-reflection
Alexandre Piché, Aristides Milios, Dzmitry Bahdanau +1
In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated wit…
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan +2
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This…
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach +3
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is only slowly adopting these models for t…