Improving ChatGPT’s Ability to Understand Ambiguous Prompts

Prompt engineering techniques are being used to help large language models (LLMs) handle pronouns and other complex coreferences in retrieval augmented generation (RAG) systems. RAG combines the power of LLMs with a vector database acting as long-term memory, enhancing the accuracy of generated responses. One example is Akcio, an open source project that offers a robust question-answer system. However, implementing RAG systems introduces challenges, particularly in multi-turn conversations involving coreference resolution. Researchers are turning to LLMs like ChatGPT for coreference resolution tasks, but they occasionally produce direct answers instead of following the prompt instructions. A refined approach using few-shot prompts and Chain of Thought (CoT) methods has been developed to guide ChatGPT through coreference resolution, resulting in coherent responses.

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