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20232025
most citedHard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

40 citations · 135 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.LG2025★ 1 cited

DynaGuard: A Dynamic Guardian Model With User-Defined Policies

Monte Hoover, Vatsal Baherwani, Neel Jain +7

Guardian models play a crucial role in ensuring the safety and ethical behavior of user-facing AI applications by enforcing guardrails and detecting harmful content. While standard…

cs.LG2025

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Jonas Geiping, Sean McLeish, Neel Jain +6

We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent bloc…

cs.LG2025

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers

Siddharth Singh, Prajwal Singhania, Aditya Ranjan +9

Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack.…

cs.LG2024★ 1 cited

Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models

Neel Jain, Aditya Shrivastava, Chenyang Zhu +6

A key component of building safe and reliable language models is enabling the models to appropriately refuse to follow certain instructions or answer certain questions. We may want…

cs.LG2024★ 6 cited

Transformers Can Do Arithmetic with the Right Embeddings

Sean McLeish, Arpit Bansal, Alex Stein +8

The poor performance of transformers on arithmetic tasks seems to stem in large part from their inability to keep track of the exact position of each digit inside of a large span o…

cs.LG2023★ 37 cited

Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Neel Jain, Avi Schwarzschild, Yuxin Wen +7

As Large Language Models quickly become ubiquitous, it becomes critical to understand their security vulnerabilities. Recent work shows that text optimizers can produce jailbreakin…

cs.LG2023★ 40 cited

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

Yuxin Wen, Neel Jain, John Kirchenbauer +3

The strength of modern generative models lies in their ability to be controlled through text-based prompts. Typical "hard" prompts are made from interpretable words and tokens, and…