Facultatea de Automatică și Calculatoare
URI permanent pentru această comunitatehttps://oasis.utcluj.app/handle/123456789/479
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Articol New optical coherence tomography biomarkers identified through deep learning for risk stratification in patients with age-related macular degeneration(2024-12-12) Adrian GrozaThis is the final technical report of the project New optical coherence tomography biomarkers identified through deep learning for risk stratification in patients with age-related macular degeneration, PN-III-P2-2.1-PED-2021-2709, 616PED/2022Articol Measuring reasoning capabilities of ChatGPT(2023-09-15) Adrian GrozaI shall quantify the logical faults generated by ChatGPT when applied to reasoning tasks. For experiments, I use the 144 puzzles from the library https://users.utcluj.ro/∼agroza/puzzles/maloga [1]. 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 [2] and the finite models finder Mace4 [3] based on human-modelling in Equational First Order Logic. A first output of this study is the benchmark of 100 logical puzzles. For this dataset ChatGPT provided both correct answer and justification for 7% only. 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 such logical faults, among which: inconsistencies, implication does not hold, unsupported claim, lack of commonsense, 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% from the generated text was a logical faultArticol C09: Clustering Algorithms(2024-11-29) Camelia LemnaruLecture on clustering and clustering algorithms.Articol Prompting Fairness: Learning Prompts for Debiasing Large Language Models(Anonymous EACL submission, pre-print, 2023-06-01) Andrei-Victor Chisca; Andrei-Cristian Rad; Camelia LemnaruLarge language models are prone to internalize social biases due to the characteristics of the data used for their self-supervised training scheme. Considering their recent emergence and wide availability to the general public, it is mandatory to identify and alleviate these biases to avoid perpetuating stereotypes towards underrepresented groups. We present a novel prompt-tuning method for reducing biases in encoder models such as BERT or RoBERTa. Unlike other methods, we only train a small set of additional reusable token embeddings that can be concatenated to any input sequence to reduce bias in the outputs. We particularize this method to gender bias by providing a set of templates used for training the prompts. Evaluations on two benchmarks show that our method is on par with the state of the art while having a limited impact on language modeling ability.Comunitate Colecție Colecție Colecție