资源简介：
---
license: apache-2.0
---
<span style="color:darkred; font-size:24px;"><b>New Paper! 🎉 🎊 </b></span>
## 🎉🎊 Released new paper on AI safety! 🎉 🎊
Check out our new paper **SafeInfer** at https://arxiv.org/abs/2406.12274 👈
Safety-aligned language models often exhibit fragile and imbalanced safety mechanisms, increasing the likelihood of generating unsafe content. In addition, incorporating new knowledge through editing techniques to language models can further compromise safety. To address these issues, we propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy for generating safe responses to user queries. SafeInfer comprises two phases: the safety amplification phase, which employs safe demonstration examples to adjust the model's hidden states and increase the likelihood of safer outputs, and the safety-guided decoding phase, which influences token selection based on safety-optimized distributions, ensuring the generated content complies with ethical guidelines. Further, we present HarmEval, a novel benchmark for extensive safety evaluations, designed to address potential misuse scenarios in accordance with the policies of leading AI tech giants.
👉 *huggingface*: https://huggingface.co/papers/2406.12274
👉 *arxiv version*: https://arxiv.org/abs/2406.12274
---
# Releasing our new paper **How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries**
👉 Read our paper at https://arxiv.org/abs/2402.15302
🎯 If you are using this dataset, please cite our papers
```
@article{DBLP:journals/corr/abs-2402-15302,
author = {Somnath Banerjee and
Sayan Layek and
Rima Hazra and
Animesh Mukherjee},
title = {How (un)ethical are instruction-centric responses of LLMs? Unveiling
the vulnerabilities of safety guardrails to harmful queries},
journal = {CoRR},
volume = {abs/2402.15302},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2402.15302},
doi = {10.48550/ARXIV.2402.15302},
eprinttype = {arXiv},
eprint = {2402.15302},
timestamp = {Fri, 22 Mar 2024 12:19:03 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2402-15302.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@misc{banerjee2024safeinfer,
title={SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models},
author={Somnath Banerjee and Soham Tripathy and Sayan Layek and Shanu Kumar and Animesh Mukherjee and Rima Hazra},
year={2024},
eprint={2406.12274},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
```
### 🌟 If you find our paper interesting and like the dataset, please support us by <span style="color:royalblue;">upvoting and sharing our work!</span> 🌟
