Summary of Language Models Resist Alignment: Evidence From Data Compression, by Jiaming Ji et al.
Language Models Resist Alignment: Evidence From Data Compression
by Jiaming Ji, Kaile Wang, Tianyi Qiu, Boyuan Chen, Jiayi Zhou, Changye Li, Hantao Lou, Josef Dai, Yunhuai Liu, Yaodong Yang
First submitted to arxiv on: 10 Jun 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 A new study explores the limitations of aligning large language models (LLMs) to prevent unwanted behaviors. Researchers found that even well-aligned models can revert to their original behavior patterns when fine-tuned, a phenomenon known as “elasticity.” This issue is particularly pronounced in larger models trained on more data. The study uses compression theory and experiments to demonstrate the impact of elasticity on model performance, showing that it can cause significant declines before reverting to pre-training patterns. The findings highlight the need for further research into addressing the inherent elasticity of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools that can help us process information more efficiently. However, they can sometimes produce unwanted results. Some researchers have been working on “aligning” these models to prevent this from happening. But a new study suggests that even when we do align the models, they can still behave badly if we fine-tune them too much. This is because the models tend to go back to their original behavior patterns. The study looked at how different-sized models and amounts of training data affect this “elasticity.” It found that bigger models trained on more data are more likely to exhibit elasticity. Overall, the study shows that we need to keep working on making sure these powerful tools don’t produce bad results. |
