Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering

• URL: http://arxiv.org/abs/2306.04508v1
• Date: Wed, 7 Jun 2023 15:20:24 GMT
• Title: Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
• Authors: Zixian Huang, Jiaying Zhou, Gengyang Xiao, Gong Cheng
• Abstract summary: In this paper, we propose a novel way of employing labeled data such as it informs LLM of some undesired output. Experiments on three multi-span question answering datasets and a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.
• Score: 9.158919909909146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Whereas the recent emergence of large language models (LLMs) like ChatGPT has exhibited impressive general performance, it still has a large gap with fully-supervised models on specific tasks such as multi-span question answering. Previous researches found that in-context learning is an effective approach to exploiting LLM, by using a few task-related labeled data as demonstration examples to construct a few-shot prompt for answering new questions. A popular implementation is to concatenate a few questions and their correct answers through simple templates, informing LLM of the desired output. In this paper, we propose a novel way of employing labeled data such that it also informs LLM of some undesired output, by extending demonstration examples with feedback about answers predicted by an off-the-shelf model, e.g., correct, incorrect, or incomplete. Experiments on three multi-span question answering datasets as well as a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.

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