Memory-assisted prompt editing to improve GPT-3 after deployment (2201.06009v7)

Abstract: Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homophone, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly. We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user's intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs. Code, data, and instructions to implement MEMPROMPT for a new task at https://www.memprompt.com/.

• The paper introduces a memory-assisted prompt editing system that uses user feedback to dynamically correct GPT-3's errors post-deployment.
• It leverages a memory repository to store corrective feedback, significantly enhancing model accuracy on lexical and ethical tasks.
• The results demonstrate over 25% improvement in ethical reasoning and notable gains in linguistic tasks without retraining the model.

Overview of MemPrompt: Memory-Assisted Prompt Editing with User Feedback

The paper "MemPrompt: Memory-assisted Prompt Editing with User Feedback" addresses the challenge of improving the accuracy of LLMs like GPT-3 without the need for expensive retraining processes. The authors propose a system where user feedback, captured in a memory repository, assists in dynamically augmenting prompts to correct the model's misunderstandings.

Key Contributions

The MemPrompt approach is distinctive in its use of user feedback to enhance the performance of an LLM. This is accomplished by pairing GPT-3 with a growing memory that documents cases where the model misinterpreted user intent, paired with corrective feedback from users. The feedback is then employed to adjust prompts for similar future queries, enhancing accuracy by avoiding previously observed errors.

Tasks and Evaluation: The methodology is evaluated on four specific tasks: two involving lexical relations and two focused on ethical reasoning. Testing is conducted with simulated user feedback to validate the system's ability to learn interactively. This interactive correction mechanism shows significant performance enhancements, proving beneficial for tasks with linguistic nuances and ethical considerations.

Implementation Details: The architecture consists of several components:

• Memory $\mathcal{M}$: Maintains key-value pairs of inputs and associated feedback.
• Interactive Prompt Engineering: In-context examples showcase how feedback can transform the model's output.
• Retrieval Mechanism: Finds feedback from similar past queries to apply to current ones.

Figure \ref{fig:running-example} in the paper illustrates the interaction process, demonstrating how MemPrompt retrieves relevant feedback and adjusts the prompt to enhance task understanding without altering the model's pre-trained state.

Results

The results from the experiments indicate substantial improvements in task accuracy. On tasks with ethical reasoning, accuracy increased by over 25% with the application of past user feedback. Similarly, for lexical tasks, MemPrompt demonstrated significant enhancements in accuracy over non-memory-assisted baselines. These results highlight MemPrompt's effectiveness in adapting model responses based on user interaction.

Implications and Future Directions

The ability to improve model performance post-deployment, without retraining, offers practical and cost-effective implications for deploying large-scale LLMs. By iteratively learning from interactions, MemPrompt paves the way for more personalized and context-aware AI systems. The system's foundational design allows for future explorations into multi-user environments and diverse application domains.

Moreover, MemPrompt opens avenues for integrating complex adaptive feedback systems where models benefit from cumulative learning. Future developments could explore more sophisticated retrieval mechanisms and memory management strategies to enhance scalability and efficiency.

Conclusion

"MemPrompt: Memory-assisted Prompt Editing with User Feedback" presents a methodologically sound approach to enhancing LLM performance through memory-augmented prompts informed by user feedback. It demonstrates that with strategic system design, it is possible to refine LLMs iteratively, contributing to the broader goal of creating smarter and personalized AI systems.