Summary of A Framework For Real-time Safeguarding the Text Generation Of Large Language Model, by Ximing Dong et al.
A Framework for Real-time Safeguarding the Text Generation of Large Language Model
by Ximing Dong, Dayi Lin, Shaowei Wang, Ahmed E. Hassan
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have revolutionized natural language processing tasks but pose ethical risks due to their ability to generate harmful content. To mitigate this, various approaches have been developed to safeguard LLMs from producing unsafe text. However, existing methods have limitations, including the need for training specific control models and proactive intervention during text generation, which lead to quality degradation and increased computational overhead. To overcome these limitations, we propose LLMSafeGuard, a lightweight framework that safeguards LLM text generation in real-time. LLMSafeGuard integrates an external validator into the beam search algorithm during decoding, rejecting candidates that violate safety constraints while allowing valid ones to proceed. We introduce a similarity-based validation approach, simplifying constraint introduction and eliminating the need for control model training. Additionally, LLMSafeGuard employs a context-wise timing selection strategy, intervening LLMs only when necessary. We evaluate LLMSafeGuard on two tasks, detoxification and copyright safeguarding, demonstrating its superior performance over SOTA baselines. LLMSafeGuard reduces the average toxic score of LLM output by 29.7% compared to the best baseline while preserving similar linguistic quality in the detoxification task. Similarly, in the copyright task, LLMSafeGuard decreases the Longest Common Subsequence (LCS) by 56.2% compared to baselines. Moreover, our context-wise timing selection strategy reduces inference time by at least 24% while maintaining comparable effectiveness as validating each time step. LLMSafeGuard also offers tunable parameters to balance its effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that big language models don’t generate harmful content, like mean or offensive text. These models have gotten very good at understanding language but can also create problems if not controlled. To solve this issue, scientists have developed different methods to keep the models from producing bad text. However, these methods have some limitations, like needing extra training and getting in the way of the model’s normal work. This paper proposes a new method called LLMSafeGuard that can quickly check what the model is generating and stop it if it’s not safe. The scientists tested this method on two tasks: making sure text isn’t toxic or mean, and preventing models from copying copyrighted content. They found that LLMSafeGuard works much better than other methods at doing these tasks without sacrificing the quality of the generated text. |
Keywords
* Artificial intelligence * Inference * Natural language processing * Text generation
