# ChatGPT and Open-AI Models: A Preliminary Review
## 3 Literature Review
### General Topics  
[PERSON] et al. explored the capabilities and limitations of ChatGPT in reasoning, factuality, hallucinations, and interactivity and compared it to earlier language models [52]. ChatGPT exhibited proficiency in deductive and abductive reasoning but faced limitations in addressing semantic reasoning tasks that were non-textual in nature. It generated invented information, but accuracy could be improved by developing an interface to an external knowledge source. ChatGPT's interactive capability has improved compared to earlier language models, and it surpassed LLMs with zero-shot learning on most tasks. However, it struggled to generate non-Latin script languages, and its accuracy in logical reasoning, non-textual reasoning, and commonsense reasoning was 64.33%. The study suggests that ChatGPT is not a dependable reasoner, and similar to other LLMs, it experienced hallucination issues.  
[PERSON] et al., 2023 investigated the efficacy of ChatGPT for automated bug fixing [53]. The study assessed ChatGPT's performance on QuixBugs, a frequently utilized benchmark dataset for bug fixing, and compared its outcomes with those of diverse methods documented in the extant literature. The results indicate that ChatGPT's bug-fixing capabilities are comparable to those of typical deep-learning approaches, such as CoCoNut and Codex, and significantly exceed the results of conventional program repair methods.  
[PERSON] et al. conducted a qualitative study on the ethical concerns associated with ChatGPT and other advanced language models (LLMs) [54]. They argued that although LLMs have influenced various industries, including copywriting and summarizing software, they may produce social biases and toxicity that pose moral and societal risks [77]. The authors examined ChatGPT's ethical dimensions, including biases, dependability, toxicity, and robustness, through experimental evaluations on multiple sample datasets. They found that current benchmarks could not adequately address a significant proportion of the ethical concerns, providing additional case studies to highlight them. The authors emphasized the importance of their findings on ChatGPT's AI ethics and offered potential future concerns and practical design considerations for LLMs. Ultimately, the authors concluded that their research provides valuable insights into future endeavors to identify and mitigate ethical risks associated with LLM applications.  
[PERSON] et al. assessed ChatGPT's comprehension capabilities by evaluating its responses against a benchmark tool known as GLUE [55]. The GLUE benchmark tool was called on to examine ChatGPT's responses to various NLU tasks, such as linguistic accept ability, sentiment analysis, paraphrasing, textual similarity, and more. The researchers compared ChatGPT with four BERT-style, fine-tuned models and found that ChatGPT demonstrated superior performance compared to the RoBERTa-large model in various tasks. In this study, ChatGPT achieved a similar level of comprehension as some BERT-style models that have been fine-tuned. However, the study also revealed that ChatGPT did not outperform the existing top models in specific natural language understanding tasks.