Papers with toxicity mitigation
Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models (2023.findings-emnlp)
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| Challenge: | Existing methods for mitigating toxicity require drastic modifications to model parameters or the use of computationally intensive auxiliary models. |
| Approach: | They propose a method that matches current state-of-the-art toxicity mitigation while achieving 43% relative latency reduction during inference. |
| Outcome: | The proposed method achieves 43% latency reduction during inference and is more computationally efficient. |
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs (2024.findings-acl)
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| Challenge: | Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. |
| Approach: | They propose a pluggable CTG framework for Large Language Models to control text . they use attribute scorers to evaluate attributes of sentences and construct dynamic attribute graphs . |
| Outcome: | The proposed framework achieves a peak improvement of 19.29% over baseline methods in two tasks. |
Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages (2024.acl-long)
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Shih-Cheng Huang, Pin-Zu Li, Yu-chi Hsu, Kuang-Ming Chen, Yu Tung Lin, Shih-Kai Hsiao, Richard Tsai, Hung-yi Lee
| Challenge: | Despite the rapid development of large language models, the language capabilities of most open-source LLMs are primarily focused on English due to data constraints. |
| Approach: | They propose a chat vector to equip pre-trained language models with instruction following and human value alignment via simple model arithmetic. |
| Outcome: | The proposed method can be extended to include various languages, base models, and chat vectors. |
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models (2024.findings-acl)
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| Challenge: | toxicity mitigation in language models has been focused on single-language settings . however, widespread adoption of LLMs has introduced a range of unknown -harms . |
| Approach: | They employ translated data to evaluate and enhance mitigation techniques in the absence of sufficient annotated datasets across languages. |
| Outcome: | The proposed approach compares translation quality and retrieval-augmented mitigation techniques under static and continual toxicity mitigation scenarios. |