Measuring Reasoning of ChatGPT; Breakthrough Architecture Exceeding Transformers; Rise of Small Language Models; Midjourney vs. DALL-E 2; and More.

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Measuring reasoning capabilities of ChatGPT : I shall quantify the logical faults generated by ChatGPT when applied to reasoning tasks. For experiments, I use the 144 puzzles from the library. The library contains puzzles of various types, including arithmetic puzzles, logical equations, Sudoku-like puzzles, zebra-like puzzles, truth-telling puzzles, grid puzzles, strange numbers, or self-reference puzzles. The correct solutions for these puzzles were checked using the theorem prover Prover9~\cite{mccune2005release} and the finite model's finder Mace4~\cite{mccune2003mace4} based on human modeling in Equational First Order Logic. The first output of this study is the benchmark of 100 logical puzzles. ChatGPT provided both correct answers and justification for 7% for this dataset. %, while BARD for 5%. Since the dataset seems challenging, the researchers are invited to test the dataset on more advanced or tuned models than ChatGPT3.5 with more crafted prompts. A second output is the classification of reasoning faults conveyed by ChatGPT. This classification forms a basis for a taxonomy of reasoning faults generated by large language models. I have identified 67 logical faults: inconsistencies, implication does not hold, unsupported claim, lack of common sense, and wrong justification. The 100 solutions generated by ChatGPT contain 698 logical faults. That is, on average, 7 fallacies for each reasoning task. A third output is the annotated answers of the ChatGPT with the corresponding logical faults. Each wrong statement within the ChatGPT answer was manually annotated, aiming to quantify the amount of faulty text generated by the language model. On average, 26.03% of the generated text was a logical fault.

Abstractive Summarization of Large Document Collections Using GPT : This paper proposes a method of abstractive summarization designed to scale document collections instead of individual documents. Our approach applies a combination of semantic clustering, document size reduction within topic clusters, semantic chunking of a cluster’s documents, GPT-based summarization and concatenation, and a combined sentiment and text visualization of each topic to support exploratory data analysis. Statistical comparison of our results to existing state-of-the-art systems BART, BRIO, PEGASUS, and MoCa using ROGUE summary scores showed statistically equivalent performance with BART and PEGASUS on the CNN/Daily Mail test dataset, and with BART on the Gigaword test dataset. This finding is promising since we view document collection summarization as more challenging than individual document summarization. We conclude by discussing how scale issues are being addressed in the GPT large language model and then suggest potential areas for future work.

Does Synthetic Data Make Large Language Models More Efficient? Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies. One challenge persistently confronting researchers is the scarcity of high-quality, annotated datasets that drive these models. This paper explores the nuances of synthetic data generation in NLP, focusing on template-based question generation. By assessing its advantages, including data augmentation potential and the introduction of structured variety, we juxtapose these benefits against inherent limitations, such as the risk of overfitting and the constraints posed by pre-defined templates. Drawing from empirical evaluations, we demonstrate the impact of template-based synthetic data on the performance of modern transformer models. We conclude by emphasizing the delicate balance between synthetic and real-world data and the future trajectories of integrating synthetic data in model training pipelines. The findings aim to guide NLP practitioners in harnessing synthetic data's potential, ensuring optimal model performance in diverse applications.

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Industry Insights

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