1.8k citations · 2.1k across the 20 of their papers we have counts for
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Forecasting Downstream Performance of LLMs With Proxy Metrics
Arkil Patel, Siva Reddy, Marius Mosbach +1
Progress in language model development is often driven by comparative decisions: which architecture to adopt, which pretraining corpus to use, or which training recipe to apply. Ma…
BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning
Ahmed Masry, Abhay Puri, Masoud Hashemi +13
Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) ha…
How to Get Your LLM to Generate Challenging Problems for Evaluation
Arkil Patel, Siva Reddy, Dzmitry Bahdanau
The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional human annotation is increasingly impractica…
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…