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Sparsely-activated Mixture-of-Experts (MoE) architecture has increasingly been adopted to further scale large language models (LLMs) due to its sub-linear scaling for computation costs. However, frequent failures still pose significant challenges as training scales. The cost of even a single failure is significant, as all GPUs need to wait idle until the failure is resolved, potentially losing considerable training progress as training has to restart from checkpoints. Existing solutions for efficient fault-tolerant training either lack elasticity or rely on building resiliency into pipeline parallelism, which cannot be applied to MoE models due to the expert parallelism strategy adopted by the MoE architecture. We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training, while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Through adaptive expert placement and a flexible token dispatcher, Lazarus can also fully utilize all available nodes after failures, leaving no GPU idle. Our evaluation shows that Lazarus outperforms existing MoE training systems by up to 5.7x under frequent node failures and 3.4x on a real spot instance trace.

Large Language Models (LLMs) have been widely deployed for their remarkable capability to generate texts resembling human language. However, they could be misused by criminals to create deceptive content, such as fake news and phishing emails, which raises ethical concerns. Watermarking is a key technique to mitigate the misuse of LLMs, which embeds a watermark (e.g., a bit string) into a text generated by a LLM. Consequently, this enables the detection of texts generated by a LLM as well as the tracing of generated texts to a specific user. The major limitation of existing watermark techniques is that they cannot accurately or efficiently extract the watermark from a text, especially when the watermark is a long bit string. This key limitation impedes their deployment for real-world applications, e.g., tracing generated texts to a specific user. This work introduces a novel watermarking method for LLM-generated text grounded in \textbf{error-correction codes} to address this challenge. We provide strong theoretical analysis, demonstrating that under bounded adversarial word/token edits (insertion, deletion, and substitution), our method can correctly extract watermarks, offering a provable robustness guarantee. This breakthrough is also evidenced by our extensive experimental results. The experiments show that our method substantially outperforms existing baselines in both accuracy and robustness on benchmark datasets. For instance, when embedding a bit string of length 12 into a 200-token generated text, our approach attains an impressive match rate of $98.4\%$, surpassing the performance of Yoo et al. (state-of-the-art baseline) at $85.6\%$. When subjected to a copy-paste attack involving the injection of 50 tokens to generated texts with 200 words, our method maintains a substantial match rate of $90.8\%$, while the match rate of Yoo et al. diminishes to below $65\%$.

Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on governments, businesses, and individuals. Also growing significantly is the heterogeneity of the ecosystem where a variety of blockchains co-exist. Cross-chain bridge is a necessary building block in this multi-chain ecosystem. Existing solutions, however, either suffer from performance issues or rely on trust assumptions of committees that significantly lower the security. Recurring attacks against bridges have cost users more than 1.5 billion USD. In this paper, we introduce zkBridge, an efficient cross-chain bridge that guarantees strong security without external trust assumptions. With succinct proofs, zkBridge not only guarantees correctness, but also significantly reduces on-chain verification cost. We propose novel succinct proof protocols that are orders-of-magnitude faster than existing solutions for workload in zkBridge. With a modular design, zkBridge enables a broad spectrum of use cases and capabilities, including message passing, token transferring, and other computational logic operating on state changes from different chains. To demonstrate the practicality of zkBridge, we implemented a prototype bridge from Cosmos to Ethereum, a particularly challenging direction that involves large proof circuits that existing systems cannot efficiently handle. Our evaluation shows that zkBridge achieves practical performance: proof generation takes less than 20 seconds, while verifying proofs on-chain costs less than 230K gas. For completeness, we also implemented and evaluated the direction from Ethereum to other EVM-compatible chains (such as BSC) which involves smaller circuits and incurs much less overhead., Comment: An extended version of the paper to appear in ACM CCS 2022