Summary of Args: Alignment As Reward-guided Search, by Maxim Khanov et al.
ARGS: Alignment as Reward-Guided Search
by Maxim Khanov, Jirayu Burapacheep, Yixuan Li
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents ARGS, a novel framework that aligns large language models with human objectives without requiring expensive reinforcement learning (RL) training. The approach integrates alignment into the decoding process by adjusting the model’s probabilistic predictions using a reward signal. This results in texts with semantic diversity while being aligned with human preferences. The authors demonstrate consistent enhancements in average reward compared to baselines across diverse alignment tasks and various model dimensions, showcasing a promising solution for aligning language models. For instance, under the same greedy-based decoding strategy, ARGS improves the average reward by 19.56% relative to the baseline and secures a preference or tie score of 64.33% in GPT-4 evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer language models better understand what humans want them to say. Currently, making these models align with human preferences is difficult and time-consuming. The researchers introduce a new way called ARGS that does this alignment while the model is generating text, without needing expensive training. This approach produces diverse texts that are more in line with what humans prefer. The results show that ARGS works well across different tasks and model sizes, promising better language models for the future. |
Keywords
* Artificial intelligence * Alignment * Gpt * Reinforcement learning
