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Background

  • Background The paper discusses the limitations encountered by Large Language Models (LLMs) such as ChatGPT and LLaMA in domain-specific tasks. These models often lack depth and accuracy in specialized areas and can see a decrease in general capabilities when fine-tuned, especially their analysis ability in smaller-sized models.

  • Existing Work Existing LLMs typically struggle to achieve in-depth analysis and precise solutions for domain-specific problems, and they tend to not reach the desired effectiveness even after being fine-tuned to optimize for these specific tasks.

Core Contributions

  • Introduced an innovative LLM named ICE-GRT

    • Challenge 1: Performance improvement in domain-specific tasks The paper describes how the application of generative transformers to reinforcement learning from human feedback (RLHF), grounded in Proximal Policy Optimization (PPO), enables ICE-GRT to perform exceptionally well in domain-specific tasks without compromising its performance on general tasks. This demonstrates the method's capacity to overcome challenges specific to domains without sacrificing the model's general capabilities.

    • Challenge 2: Maintaining and improving detailed analysis capabilities ICE-GRT introduces a model that can not only generate robust answers but also provide detailed analyses of the reasons behind the answers. This capability allows ICE-GRT to surpass models fine-tuned only with supervision, specifically addressing the issue that existing models cannot perform in-depth analysis, especially small LLMs.

Implementation and Deployment

The paper provides an in-depth introduction to the overall training process and key components of the ICE-GRT model, including a mathematically formalized depiction of training strategies, optimization, and learning mechanisms. Using a scheme that blends domain-specific and general task data, the model is trained through a meticulous human annotation process, ensuring training on high-quality data. The model has consistently achieved state-of-the-art performance across 12 well-known public LLM evaluation benchmarks and handled various domain-specific tasks effectively, such as poem generation, text-to-table conversions, engaging in multiple round dialogues, generating accurate multilingual responses, etc. These results highlight the effectiveness of the ICE-GRT method and its potential impact on the field of NLP.

Summary

This paper introduces a methodology, ICE-GRT, designed to enhance the depth and accuracy of LLMs in handling domain-specific tasks. By incorporating reinforcement learning from human feedback, ICE-GRT significantly improves domain-specific capabilities without sacrificing general task performance, achieving state-of-the-art in several assessment tasks.