Can LLM s Understand the Implication of Emphasized Sentences in Dialogue? (2024)

Abstract: Emphasis is a crucial component in human communication, which indicates speaker’s intention and implication beyond pure text in dialogue. While Large Language Models (LLMs) have revolutionized natural language processing, their ability to understand emphasis in dialogue remains uncertain. This paper introduces Emphasized-Talk, a benchmark dataset with annotated dialogue samples capturing the implications of emphasis. We evaluate various LLMs, both open-source and commercial, to assess their performance in understanding and generating emphasis. Additionally, we propose an automatic evaluation pipeline using GPT-4, which achieve high correlation with human scoring. Our findings reveal that although commercial LLMs generally perform better, there is still significant room for improvement in comprehending emphasized sentences.

## introduction emphasis plays a key role in communication by highlighting important parts of dialogue. recognizing these emphatic cues is crucial for achieving natural interactions, as they help reinforce intentions, and express nuances essential for understanding conversations. for instance, altering the emphasis in the sentence i never said he stole my bag from he to my can drastically change its meaning. emphasis can be represented in various ways, such as quotation marks, bold text, italics, capitalization, and underlining. large language models (llms) @xcite @xcite have revolutionized natural language processing by leveraging vast amounts of unlabeled text data. they can generate and understand natural language accurately, capturing nuances and complex relationships. as a result, llms are widely used in applications such as dialogue systems and virtual 1 https://github.com/daniellin94144/emphasized-talk assistants. despite their capabilities, the ability of llms to grasp the subtle nuances and complex meanings conveyed through emphasis in human dialogue remains unclear. to address this gap, we introduce a novel benchmark evaluation dataset, emphasized-talk, which features real dialogue samples with annotations capturing the implications of emphasis as interpreted by humans. emphasized-talk includes the same dialogue context and current sentence but with different words or phrases emphasized. this dataset is designed to test whether llms can accurately understand the meaning and intention behind emphasis in dialogue. note that in this work, we use quotation mark ("") to highlight the emphasis in text sentences. our study includes both opensourced and commercial llms of varying sizes to provide a comprehensive analysis of their current capabilities and limitations. furthermore, we propose an automatic evaluation method that leverages the capabilities of @xcite to assess the performance of these models. by comparing various automatic evaluation methods, we find that gpt-4's evaluations correlate well with human evaluation scores. our findings indicate that open-source llms generally struggle to understand the implications of emphasized sentences. while commercial llms perform better, there is still room for improvement in their comprehension of emphasized text. our contributions can be summarized as below: ## related works human communication beyond textual information: in addition to text, the extra tags like emotion @xcite , emoji @xcite , sentiment @xcite , prosody @xcite , and non-lexical verbal sounds @xcite are critical in understanding and generating human dialogue. these cues provide essential context and pragmatic use to the interaction, often conveying nuances that textual information alone cannot fully capture @xcite . for instance, emotions and sentiment can indicate the speaker's attitude and feelings, while speaking style and prosody can reveal additional layers of meaning, such as sarcasm, emphasis, or urgency. this work focuses on emphasis as the non-textual cue in human communication, aiming to understand the implication meaning of beyond text. the quotation mark ("") is used to simulate the expression of emphasis. ## conclusion this work explores the ability of llms to interpret emphasized text in dialogues. we introduced the novel emphasized-talk dataset with the same dialogue context and current turn sentence with different emphasized words or phrases. furthermore, we study the automatic evaluation methods using gpt-4, showing a high correlation between human rating and auto-gpt4. our analysis shows that while commercial llms outperform open-sourced models, there is still room for improvement. we encourage future llm research to evaluate the model on the proposed benchmark and automatic evaluation method to enhance the llm's dialogue ability.