9 citations · 13 across the 8 of their papers we have counts for
11 papers
Where Should a Document Live: Context, Representations, or Parameters?
Nathanaël Carraz Rakotonirina, Momchil Hardalov, Gonzalo Iglesias +1
To answer questions outside of their pre-training data, large language models (LLMs) need access to new information, which can be presented in the context window as documents, enco…
Correct, Concise and Complete: Multi-stage Training For Adaptive Reasoning
Nathanaël Carraz Rakotonirina, Ren Pang, Neha Anna John +2
The reasoning capabilities of large language models (LLMs) have improved substantially through increased test-time computation, typically in the form of intermediate tokens known a…
From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions
Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos +5
Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also…
Evil twins are not that evil: Qualitative insights into machine-generated prompts
Nathanaël Carraz Rakotonirina, Corentin Kervadec, Francesca Franzon +1
It has been widely observed that language models (LMs) respond in predictable ways to algorithmically generated prompts that are seemingly unintelligible. This is both a sign that…
MemoryPrompt: A Light Wrapper to Improve Context Tracking in Pre-trained Language Models
Nathanaël Carraz Rakotonirina, Marco Baroni
Transformer-based language models (LMs) track contextual information through large, hard-coded input windows. We introduce MemoryPrompt, a leaner approach in which the LM is comple…
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
Elena Raponi, Nathanael Rakotonirina Carraz, Jérémy Rapin +2
The growing ubiquity of machine learning (ML) has led it to enter various areas of computer science, including black-box optimization (BBO). Recent research is particularly concern…