Evaluating LLM Responses: The Role of Self-Reflection
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Chapter 1: Understanding LLM Grounding
When utilizing Large Language Models (LLMs), it can be challenging to assess the quality of their generated outputs, especially in the absence of LLM Grounding. But what does LLM Grounding entail?
LLM Grounding refers to the association of LLM outputs with real-world information, which facilitates more precise responses. For specialized use cases, we can allow the LLM access to our private data repositories, enhancing the relevance of its answers. In such scenarios, the LLM can pull pertinent information from these repositories to inform its responses.
Grounding diminishes the likelihood of hallucinations and forges a connection between the LLM's language capabilities and reasoning skills, utilizing a dataset that lies outside the model's inherent knowledge. Many discussions, including various blogs and YouTube videos, have explored the construction of Retrieval-Augmented Generation (RAG) systems. RAG involves grounding LLMs with tailored or proprietary data not readily accessible online.
Section 1.1: The Challenge of Generic Use Cases
You might wonder why self-reflection is necessary if grounding enhances LLM output quality. Grounding is effective only when the LLM is equipped with specific tools or data to reference. In more generalized situations, this is not always achievable, leading to uncertainty regarding the validity of the LLM's responses.
While Reinforcement Learning from Human Feedback (RLHF) does filter out many irrelevant or inappropriate responses, there remains a possibility that some may slip through the cracks of human evaluation. Ultimately, the feedback on LLM outputs is curated by humans, who are prone to errors.
We have seen similar issues in recent examples, such as with Google's AI Search overviews.
For instance, an LLM-generated response suggested mixing non-toxic glue into a sauce to enhance its texture for a query about why cheese does not adhere to pizza. This issue stems not from the LLM itself but from the underlying data, which should ideally have been filtered out during the RLHF process. However, the diversity of human backgrounds and beliefs complicates the filtering process.
Section 1.2: Potential Solutions
One feasible solution is to update the training dataset and re-fine-tune the LLM. Unfortunately, this approach is labor-intensive, costly, and time-consuming.
Given that LLMs possess a comprehensive understanding of the world within their weights and excel at reasoning, we can engage them to evaluate their own responses by providing the necessary tools. This evaluation should consider multiple aspects, including safety, ethics, relevance, structure, and clarity.
The LLM can be instructed to generate a series of reasoning steps for each evaluation criterion and assign scores accordingly. The final score will determine whether the output is deemed safe, relevant, and ethical. If the output fails to meet these standards, it won't be displayed; if it passes, it will be presented.
This concept of self-evaluation through scoring has been discussed in various academic papers, demonstrating its efficacy in refining LLM responses.
LLMs can learn self-restraint through iterative self-reflection. This video discusses how self-reflection can improve the quality of LLM outputs.
Chapter 2: Implementing Self-Reflection
To implement this self-reflective evaluation, the LLM-generated output can undergo a process of self-reflection. During this phase, the LLM is also permitted to utilize various tools, such as search functionalities, to verify the accuracy of its responses.
If the output is deemed useful—meaning it is safe, moral, ethical, and non-harmful—it will be displayed. Otherwise, the LLM will collect justifications for marking the response as unhelpful and will prompt the user for further instructions to generate a new output. Should the LLM still fail to produce a satisfactory response after several attempts, it will issue an apology, stating its inability to assist further.
We'll focus on the self-reflection component that can be integrated into any system, utilizing the GPT-4o model as the judge for self-evaluation.
Section 2.1: The Evaluation Process
Our evaluation process employs function calling alongside the chain-of-thought prompting technique as part of the LLM-judge's self-reflection methodology.
The system prompt for the self-evaluation process is as follows:
SYSTEM_PROMPT = """Review the user's question and the corresponding response using the additive 5-point scoring system described below...
"""
This prompt instructs the LLM to assess its output based on specific criteria, adding or deducting points accordingly.
Discover how the new LLM outperforms other models with its "self-healing" capabilities in this insightful video.
Conclusion
In this discussion, we examined LLM Grounding and the limitations of applying it to generic use cases. We also explored the concept of using LLMs as self-judges to evaluate their outputs through self-reflection. By coding an LLM-as-a-judge and applying it to various questionable Google AI Overview responses, we observed its effectiveness in filtering out irrelevant, harmful, or unethical content.