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Publications about AI methods and applications.
Additional file 1: Supplementary Table 1. Years of Experience of Human Participants from Standardized Test.
Research paper by Zhexin Zhang and collaborators. Published 2025-02-24. Topics: AI safety, AISafetyLab, attack methodologies, defense methodologies, evaluation methodologies.
Research paper by Aly M. Kassem and collaborators. arXiv record dated 2025-02-09. Research area: Language technology.
The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones. In recent times, a number of models have emerged, claiming performance near that of GPT-3.5 or GPT-4 through various instruction-tuning methods. As practitioners of Text-to-SQL parsing, we are grateful for their valuable contributions to open-source research. However, it is important to approach these claims with a sense of scrutiny and ascertain the actual effectiveness of these models. Therefore, we pit six popular large language
Research paper by Swarnadeep Saha and collaborators. Published 2023-10-23. Topics: Large Language Models, LLMs, branch, solve, merge.
Research paper by Chenghao Fan and collaborators. arXiv record dated 2025-04-17. Research area: Language technology.
Electronic health records (EHRs) house crucial patient data in clinical notes. As these notes grow in volume and complexity, manual extraction becomes challenging. This work introduces a natural language interface using large language models (LLMs) for dynamic question-answering on clinical notes. Our chatbot, powered by Langchain and transformer-based LLMs, allows users to query in natural language, receiving relevant answers from clinical notes. Experiments, utilizing various embedding models and advanced LLMs, show Wizard Vicuna's superior accuracy, albeit with high compute demands. Model optimization, including weight quantization, improv
Research paper by Ning Ding and collaborators. Published 2023-05-23. Topics: Llama model, Vicuna, conversational model, large-scale dataset, instructional conversations.
Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating the effect of PTQ on Weight, Activation, and KV Cache on 11 model families, including OPT, LLaMA2, Falcon, Bloomz, Mistral, ChatGLM, Vicuna, LongChat, St
Recently, Instruction fine-tuning has risen to prominence as a potential method for enhancing the zero-shot capabilities of Large Language Models (LLMs) on novel tasks. This technique has shown an exceptional ability to boost the performance of moderately sized LLMs, sometimes even reaching performance levels comparable to those of much larger model variants. The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks. We conducted an exploration of six models including Alpaca, Vicuna, WizardLM, and Traditional Task-oriented Models(Flan-T5-XL/XXL, T0++) using real-world relation extraction datasets as case studies. We ca
Research paper by Deepanway Ghosal and collaborators. Published 2023-07-05. Topics: INSTRUCTEVAL, large language models, encoder-decoder, decoder-only, T5-based.
Research paper by Lichang Chen and collaborators. Published 2023-06-05. Topics: large language models, LLMs, zero-shot evaluation, Bayesian optimization, soft prompt.
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