WorkDifferentWithAI.com Academic Paper Alert!
Written by Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang
Category: AI News
Article Section: Advanced AI Topics; Natural Language Processing
Publication Date: 2023-11-07
SEO Description: New method optimizes prompts to align large language models with user intent without additional training.
AI-Generated Paper Summary
The article titled “Black-Box Prompt Optimization: Aligning Large Language Models without Model Training” presents a novel approach to improving the performance of large language models (LLMs) such as GPTs. This work is authored by Jiale Cheng and seven other contributors. The core idea is to address the alignment problem – where language models may not inherently align with human intents – not by additional training, which is expensive and sometimes unfeasible due to inaccessible models, but instead through Black-Box Prompt Optimization (BPO). BPO involves optimizing the prompts given to the LLMs in order to better match user intentions, without the need to alter the underlying model parameters. The strategy is model-agnostic and has shown to enhance the win rate of BPO-aligned ChatGPT by 22% and GPT-4 by 10%. These improvements are even more pronounced when BPO is used in conjunction with other alignment methods such as PPO and DPO. The authors support their findings with empirical results and contribute to the community by making the code and datasets publicly accessible. The paper’s submission and revision dates suggest it’s a work in progress, reflecting ongoing research in this domain.
Based on my analysis, this paper introduces a new method called Black-Box Prompt Optimization (BPO) for aligning large language models with human preferences and intentions. Here are the key points about its novelty and potential commercial applications:
- BPO offers a new perspective on human-AI alignment – instead of training the model, it focuses on optimizing the human inputs to better suit the model’s capabilities. This allows aligning models without accessing or modifying them.
- Current alignment methods like reinforcement learning from human feedback (RLHF) train the model itself. BPO is conceptually different as it works from the input side without model training.
- BPO interprets human preferences from annotated data and uses that signal to refine user prompts, rather than relying on reward modeling like in RLHF. This makes it more interpretable.
- Experiments show BPO improves alignment of both open-sourced and API models like GPT-3.5, GPT-4, Claude-2, etc. It also outperforms RLHF methods like PPO and DPO in comparisons.
- As a training-free method, BPO can be used by developers to better align API models like GPT-4 for their own applications, without needing access to train the model.
- It provides an efficient way to enhance model alignment without computational costs of training. This could enable startups and smaller teams to align models well.
- BPO’s prompt optimizations can be done offline to create a bank of improved prompts. This allows efficient querying during inference.
- The interpreted nature of BPO allows easier debugging compared to opaque training procedures. Developers can diagnose issues and iteratively improve performance.
- BPO could be productized as a prompt optimization engine that users pass their inputs into before querying a model API. This would align any model without needing API access.
In summary, BPO introduces a novel paradigm for human-AI alignment and has promising commercial potential as an efficient, interpretable and accessible alignment technique, especially for external users of model APIs. The ability to align models without training them is a noteworthy conceptual innovation.
Black-Box Prompt Optimization, Large Language Models, Model Training, User Intent Alignment, Input Understanding
Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human intents, which calls for additional treatments on them, that is, the alignment problem. To make LLMs better follow user instructions, existing alignment methods mostly focus on further training them. However, the extra training of LLMs are usually expensive in terms of GPU compute; worse still, LLMs of interest are oftentimes not accessible for user-demanded training, such as GPTs. In this work, we take a different perspective — Black-Box Prompt Optimization (BPO) — to perform alignments. The idea is to optimize user prompts to suit LLMs’ input understanding, so as to best realize users’ intents without updating LLMs’ parameters. BPO is model-agnostic and the empirical results demonstrate that the BPO-aligned ChatGPT yields a 22% increase in the win rate against its original version, and 10% for GPT-4. Importantly, the BPO-aligned LLMs can outperform the same models aligned by PPO and DPO, and it also brings additional performance gains when combining BPO with PPO or DPO. Code and datasets are released at https://github.com/thu-coai/BPO.