Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models
Probabilistic models of language understanding are valuable tools for investigating human language use. However, they need to be hand-designed for a particular domain. In contrast, large language models (LLMs) are trained on text that spans a wide array of domains, but they lack the structure and interpretability of probabilistic models. In this paper, we use chain-of-thought prompts to introduce structures from probabilistic models into LLMs. We explore this approach in the case of metaphor understanding. Our chain-of-thought prompts lead language models to infer latent variables and reason about their relationships in order to choose appropriate paraphrases for metaphors. The latent variables and relationships chosen are informed by theories of metaphor understanding from cognitive psychology. We apply these prompts to the two largest versions of GPT-3 and show that they can improve performance in a paraphrase selection task.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevance
Metaphorical expressions are abundant in Traditional Chinese Medicine (TCM), conveying complex disease mechanisms and holistic health concepts through culturally rich and often abstract terminology. Bridging these metaph…
Decision MakingDutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop
Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients.…
Few-Shot LearningDecision MakingGUIDE-CoT: Goal-driven and User-Informed Dynamic Estimation for Pedestrian Trajectory using Chain-of-Thought
While Large Language Models (LLMs) have recently shown impressive results in reasoning tasks, their application to pedestrian trajectory prediction remains challenging due to two key limitations: insufficient use of visu…
Pedestrian Trajectory PredictionPredictionTrajectory PredictionDRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
Recently, O1-like models have emerged as representative examples, illustrating the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an att…
Machine TranslationMathTranslationPsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models
Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpful. In high-risk interactions involving …
parameter-efficient fine-tuningDomain Generalization