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Eric Hambro

8 accepted papers

2024

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Alexander Havrilla, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro +1
ICML 2024poster

State-of-the-art language models can exhibit reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best models struggle to identify *when and where to refine* without access to external feedback. In this paper, we propose Stepwise ORMs (*…

Cited by 52Source ↗PDF ↗Scholar ↗
2024

Generalization to New Sequential Decision Making Tasks with In-Context Learning

Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu
ICML 2024poster

Training autonomous agents that can learn new tasks from only a handful of demonstrations is a long-standing problem in machine learning. Recently, transformers have been shown to learn new language or vision tasks without any weight updates from only a few examples, also referred to as in-context l…

Cited by 20Source ↗PDF ↗Scholar ↗
2024

Know When To Stop: A Study of Semantic Drift in Text Generation

Ava Spataru, Eric Hambro, Elena Voita, Nicola Cancedda
NAACL 2024long

In this work, we explicitly show that modern LLMs tend to generate correct facts first, then “drift away” and generate incorrect facts later: this was occasionally observed but never properly measured. We develop a semantic drift score that measures the degree of separation between correct and incor…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts

Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt +6
NeurIPS 2024poster

As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramount importance. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, o…

Cited by 71Source ↗PDF ↗Scholar ↗
2024

Understanding the Effects of RLHF on LLM Generalisation and Diversity

Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette +1
ICLR 2024poster

Large language models (LLMs) fine-tuned with reinforcement learning from human feedback (RLHF) have been used in some of the most widely deployed AI models to date, such as OpenAI's ChatGPT or Anthropic's Claude. While there has been significant work developing these methods, our understanding of th…

Cited by 121Source ↗PDF ↗code ↗Scholar ↗
2023

Toolformer: Language Models Can Teach Themselves to Use Tools

Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro +3
NeurIPS 2023oral

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel. In…

Cited by 1684Source ↗PDF ↗Scholar ↗
2022

Dungeons and Data: A Large-Scale NetHack Dataset

Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Kuttler +1
NeurIPS 2022accept

Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets. However, progress on this research has been hindered by the scarcity of open-sourced datase…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2021

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro +4
NeurIPS 2021poster

Progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted by the community are not explicitly designed for evaluating specific capabilities of RL methods. While there exist enviro…

Cited by 108Source ↗code ↗Scholar ↗