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Sharath Chandra Raparthy

6 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

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 ↗
2023

Multi-Objective GFlowNets

Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret +1
ICML 2023poster

We study the problem of generating *diverse* candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. M…

Cited by 84Source ↗PDF ↗code ↗Scholar ↗
2022

Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie
ICLR 2022spotlight

Multi-head, key-value attention is the backbone of transformer-like model architectures which have proven to be widely successful in recent years. This attention mechanism uses multiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search - sel…

Cited by 27Source ↗PDF ↗code ↗Scholar ↗
2022

Continual Learning In Environments With Polynomial Mixing Times

Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Raaj Subbaraj, Maximilian Puelma Touzel, Irina Rish
NeurIPS 2022accept

The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mixing times on learning in continual reinforcement learning (RL) remains underexplored. In this paper, we characterize problems that are of long-term interest to…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗