Dec 12, 2023 · Abstract:Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality.

Abstract. Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality.

Figure 1: Illustration of Diversified Preferences. Left: reward accuracy on each preference. Middle: the reward distribution of each RM on harmless ...

Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality.

Apr 17, 2024 · Abstract ... Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality.

4 days ago · The LLMs learn from this preference data to produce responses that better match human preferences, effectively addressing the challenge of ...

Run · 1. Reward model training · 2. Reject Sampling Inference · 3. Reject Sampling Training · 4. Language Model Inference · 5. GPT Evaluation.

Jun 2, 2024 · The method aligns LLMs with diverse user preferences using a unique system message protocol and the MULTIFACETED COLLECTION dataset, ensuring high performance ...

Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs.