LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language

Large Language Models represent state-of-the-art linguistic models designed to equip computers with the ability to comprehend natural language. With its exceptional capacity to capture complex contextual relationships, the LLaMA (Large Language Model Meta AI) family represents a novel advancement in the field of natural language processing by releasing foundational models designed to improve the natural language understanding abilities of the transformer architecture thanks to their large amount of trainable parameters (7, 13, and 70 billion parameters). In many natural language understanding tasks, these models obtain the same performances as private company models such as OpenAI Chat-GPT with the advantage to make publicly available weights and code for research and commercial uses. In this work, we investigate the possibility of Language Adaptation for LLaMA models, explicitly focusing on addressing the challenge of Italian Language coverage. Adopting an open science approach, we explore various tuning approaches to ensure a high-quality text generated in Italian suitable for common tasks in this underrepresented language in the original models' datasets. We aim to release effective text generation models with strong linguistic properties for many tasks that seem challenging using multilingual or general-purpose LLMs. By leveraging an open science philosophy, this study contributes to Language Adaptation strategies for the Italian language by introducing the novel LLaMAntino family of Italian LLMs

LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language

Large Language Models represent state-of-the-art linguistic models designed to equip computers with the ability to comprehend natural language. With its exceptional capacity to capture complex contextual relationships, the LLaMA (Large Language Model Meta AI) family represents a novel advancement in the field of natural language processing by releasing foundational models designed to improve the natural language understanding abilities of the transformer architecture thanks to their large amount of trainable parameters (7, 13, and 70 billion parameters). In many natural language understanding tasks, these models obtain the same performances as private company models such as OpenAI Chat-GPT with the advantage to make publicly available weights and code for research and commercial uses. In this work, we investigate the possibility of Language Adaptation for LLaMA models, explicitly focusing on addressing the challenge of Italian Language coverage. Adopting an open science approach, we explore various tuning approaches to ensure a high-quality text generated in Italian suitable for common tasks in this underrepresented language in the original models' datasets. We aim to release effective text generation models with strong linguistic properties for many tasks that seem challenging using multilingual or general-purpose LLMs. By leveraging an open science philosophy, this study contributes to Language Adaptation strategies for the Italian language by introducing the novel LLaMAntino family of Italian LLMs

Adaptation of the STARR test for adult Italian population: a speech test for a realistic estimate in real-life listening conditions

Objectives: To introduce the Italian adaptation of the STARR test based on a roving-level adaptive method to mimic challenging real-life listening conditions for use in people with auditory prostheses. Design: Normative data were collected and interlist-variability, as well as learning effects, were investigated using a within-subject design with repeated measures. Study sample: A group of 32 normal-hearing (NH) adults participated in the study. Results: The average speech reception threshold (SRT) for NH subjects was 8.4 dB SNR. The variability of mean SRTs across test lists was relatively small ( 1 dB for all test lists). The statistically significant differences between lists were eliminated after applying correction factors. On the basis of variability for the corrected SRTs within each subject, a difference of 2.8 dB in SRT was meaningful for outcome comparisons using one test list per condition and 2 dB using two lists per condition. Statistical analysis did not show any significant learning effects. Conclusions: Findings in NH listeners suggested that the Italian STARR test could be a promising supplement to existing speech assessment tools. Further studies in populations with hearing impairment could contribute to cross-language studies

Distortion product otoacoustic emissions in otosclerosis: intraoperative findings

The aim of the study was to investigate changes in middle ear dynamic characteristics caused by both otosclerosis and stapes surgery (platinotomy, prosthesis positioning, ossicular chain maneuver) and to evaluate distortion product otoacoustic emissions (DPOAEs) before and following surgery. The study included 15 patients (12 women, 3 men; mean age 51 years; range 32-69 years) with advanced otosclerosis. All the patients were evaluated with the use of pure-tone audiograms (preoperatively, 5 and 30 days after surgery), stapedial reflexes (preoperatively), and DPOAE recordings (preoperatively, at the end of surgery, and 5 and 30 days after surgery). Changes in the hearing thresholds and in the DPOAE amplitudes were compared. Preoperative tests showed conductive hearing loss, with a mean air-bone gap of 36.6 dB HL ranging from 0.25 to 1 kHz, and no stapedial reflexes were detected. DPOAEs were not measurable preoperatively, and they were detected only in 2 patients at the end of surgery, with low amplitudes in a narrow frequency range. No significant changes occurred in DPOAEs 5 days postoperatively. A month after surgery, improvement in conductive hearing loss was observed; the mean air-bone gap from 0.25 to 1 kHz was 12.9 dB HL, whereas the higher frequencies were still affected by the disease. DPOAEs increased in amplitude in 4 patients, but this was not significant. It remains unclear why DPOAEs are not detected despite a subjective hearing improvement and a sufficiently closed air-bone gap at least in middle and low frequencies. The results of our study show that DPOAEs cannot replace behavioral threshold tests; they may only be included in a battery of tests for a complete clinical follow-up for efficiency monitoring after stapes surgery. Copyright (c) 2007 S. Karger AG, Basel

Audiologia

Capitolo di audiologia con cenni di anatomia e fisiologia dell'apparato uditivo, vestibologia, semeiotica audiologica, semeiotica vestibolare ed inquadramento generale delle ipoacusie

Adequate formal language performance in unilateral cochlear implanted children: is it indicative of complete recovery in all linguistic domains? insights from referential communication

Objectives Referential communication (RC) is a key element in achieving a successful communication. This case series aimed to evaluate RC in children with unilateral cochlear implants (CIs) with formal language skills within the normal range. Methods and materials A total of 31 children with CIs, with language development within the normal range, were assessed using the Pragmatic Language Skills test (MEDEA). Results Of the children with CIs, 83.9% reached performance levels appropriate for their chronological ages. The results confirmed a positive effect of cochlear implantation on RC development, although difficulties remained in some CI users. Conclusions The outcomes emphasize the need to pay greater attention to the pragmatic aspects of language, assessing them with adequate testing in the early phase after cochlear implantation. Clear knowledge of children's communicative competence is the key in optimizing their communicative environments in order to create the basis for future successful interpersonal exchanges and social integration