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Manner implicatures in large language models.

¹School of Languages and Cultures, Purdue University, West Lafayette, 47907, USA. cong4@purdue.edu.

Scientific Reports

|November 24, 2024

Summary

Large language models (LLMs) struggle to understand pragmatic reasoning, specifically manner implicature, which involves inferring meaning from what is unsaid. Current models lack robust sensitivity to nuanced shades of meaning in everyday conversations.

Keywords:

Conversational implicatures Explainability Large language models Natural language understanding Pragmatic reasoning Semantics

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Area of Science:

Computational Linguistics
Artificial Intelligence
Pragmatics

Background:

Human conversation relies on inferring meaning beyond literal semantics.
Pragmatic reasoning, particularly manner implicature, is crucial for understanding implied meanings.
The ability of large language models (LLMs) to grasp these nuances is largely unexplored.

Purpose of the Study:

To investigate the extent to which pre-trained LLMs can identify and differentiate shades of meaning in manner implicature.
To evaluate LLMs' pragmatic reasoning capabilities concerning implied meanings in human conversation.

Main Methods:

Constructed three metrics: LLM-surprisals, embedding vector similarities, and natural language prompting.
Assessed LLMs' sensitivity to different dimensions of manner implicature.
Compared contextual LLM embeddings with static GloVe embeddings.

Main Results:

LLMs showed above-chance accuracy in differentiating neutral relations from entailment/implications but lacked robustness in nuanced comparisons.
Contextual embeddings offered minimal advantage over static GloVe embeddings, with no significant performance difference.
Natural language prompting provided no further evidence of LLMs' competence in representing subtle meanings.

Conclusions:

Current pre-training paradigms do not equip LLMs with significant competence in manner implicature.
Further research is needed on dataset design and benchmark metrics informed by linguistic theories to improve LLM pragmatic understanding.