Summary of Deal: Decoding-time Alignment For Large Language Models, by James Y. Huang et al.
DeAL: Decoding-time Alignment for Large Language Models
by James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, Dan Roth
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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) are designed to generate content aligned with human preferences. Current methods focus on alignment during model training using Reinforcement Learning with Human Feedback (RLHF). However, it is unclear whether these approaches are effective in teaching alignment objectives to the model. The limitations of current methods include difficulty incorporating multiple custom rewards and reliance on a single developer’s view of universal principles. Additionally, there are concerns about residual gaps in model training and reliability. To address these issues, we propose DeAL, a framework that allows customization of reward functions and enables Decoding-time Alignment of LLMs (DeAL). Our framework views decoding as a heuristic-guided search process and facilitates the use of various alignment objectives. We demonstrate the effectiveness of DeAL in achieving fine-grained trade-offs, improving adherence to alignment objectives, and addressing residual gaps in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can write like humans. But they need to learn what we consider “good” content. Right now, people use special tricks during training to help the model understand what’s good or bad. However, this isn’t perfect because it’s hard to tell the model exactly what we want. We also worry that the model might not fully understand what we’re trying to teach it. To fix these problems, scientists created a new way called DeAL (Decoding-time Alignment of LLMs). This helps us customize what we want the model to write and makes sure it follows our rules. It’s like giving the model a special set of instructions to follow. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning » Rlhf
