A survey and position paper that reviews LLM prompting, RAG, and RL techniques and argues they could support open-ended implementation generation, without presenting new results.
Reasoning Implicit Sentiment with Chain-of-Thought Prompting
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abstract
While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA) the opinion cues come in an implicit and obscure manner. Thus detecting implicit sentiment requires the common-sense and multi-hop reasoning ability to infer the latent intent of opinion. Inspired by the recent chain-of-thought (CoT) idea, in this work we introduce a Three-hop Reasoning (THOR) CoT framework to mimic the human-like reasoning process for ISA. We design a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion, and finally the sentiment polarity. Our THOR+Flan-T5 (11B) pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup. More strikingly, THOR+GPT3 (175B) boosts the SoTA by over 50% F1 on zero-shot setting. Our code is open at https://github.com/scofield7419/THOR-ISA.
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An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems
A survey and position paper that reviews LLM prompting, RAG, and RL techniques and argues they could support open-ended implementation generation, without presenting new results.