Papers with GPT-3.5 models

2 papers
Entity Tracking in Language Models (2023.acl-long)

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Challenge: Existing studies on the ability of large language models to track discourse entities have not been conducted.
Approach: They propose to investigate whether large language models can track entities . they first investigate whether Flan-T5, GPT-3 and GPT-3.5 can track the state of entities based on an English description of the initial state and a series of state-changing operations.
Outcome: The proposed task investigates whether language models can track entities based on language descriptions and state-changing operations.
Prompted Opinion Summarization with GPT-3.5 (2023.findings-acl)

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Challenge: Recent years have seen several shifts in summarization research, including extractive models.
Approach: They propose a pipeline method for applying GPT-3.5 to summarize user reviews . they propose three new metrics targeting faithfulness, factuality, and genericity .
Outcome: The proposed methods perform well in opinion summarization, the authors show . they also show that standard evaluation metrics do not reflect this performance .

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