15 research outputs found
TCBERT: A Technical Report for Chinese Topic Classification BERT
Bidirectional Encoder Representations from Transformers or
BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various
NLP tasks due to its remarkable performance. Variants customized for different
languages and tasks are proposed to further improve the performance. In this
work, we investigate supervised continued
pre-training~\cite{gururangan-etal-2020-dont} on BERT for Chinese topic
classification task. Specifically, we incorporate prompt-based learning and
contrastive learning into the pre-training. To adapt to the task of Chinese
topic classification, we collect around 2.1M Chinese data spanning various
topics. The pre-trained Chinese Topic Classification BERTs (TCBERTs) with
different parameter sizes are open-sourced at
\url{https://huggingface.co/IDEA-CCNL}
CMB: A Comprehensive Medical Benchmark in Chinese
Large Language Models (LLMs) provide a possibility to make a great
breakthrough in medicine. The establishment of a standardized medical benchmark
becomes a fundamental cornerstone to measure progression. However, medical
environments in different regions have their local characteristics, e.g., the
ubiquity and significance of traditional Chinese medicine within China.
Therefore, merely translating English-based medical evaluation may result in
\textit{contextual incongruities} to a local region. To solve the issue, we
propose a localized medical benchmark called CMB, a Comprehensive Medical
Benchmark in Chinese, designed and rooted entirely within the native Chinese
linguistic and cultural framework. While traditional Chinese medicine is
integral to this evaluation, it does not constitute its entirety. Using this
benchmark, we have evaluated several prominent large-scale LLMs, including
ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical
domain. It is worth noting that our benchmark is not devised as a leaderboard
competition but as an instrument for self-assessment of model advancements. We
hope this benchmark could facilitate the widespread adoption and enhancement of
medical LLMs within China. Check details in
\url{https://cmedbenchmark.llmzoo.com/}
AceGPT, Localizing Large Language Models in Arabic
This paper explores the imperative need and methodology for developing a
localized Large Language Model (LLM) tailored for Arabic, a language with
unique cultural characteristics that are not adequately addressed by current
mainstream models like ChatGPT. Key concerns additionally arise when
considering cultural sensitivity and local values. To this end, the paper
outlines a packaged solution, including further pre-training with Arabic texts,
supervised fine-tuning (SFT) using native Arabic instructions and GPT-4
responses in Arabic, and reinforcement learning with AI feedback (RLAIF) using
a reward model that is sensitive to local culture and values. The objective is
to train culturally aware and value-aligned Arabic LLMs that can serve the
diverse application-specific needs of Arabic-speaking communities.
Extensive evaluations demonstrated that the resulting LLM called `AceGPT' is
the SOTA open Arabic LLM in various benchmarks, including instruction-following
benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark
(i.e., Arabic MMLU and EXAMs), as well as the newly-proposed Arabic cultural \&
value alignment benchmark. Notably, AceGPT outperforms ChatGPT in the popular
Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark's limited
scale. % Natural Language Understanding (NLU) benchmark (i.e., ALUE)
Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.Comment: https://github.com/FreedomIntelligence/AceGP
Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study.
In an effort for early assessment of treatment response, we investigate radiation induced changes in quantitative CT features of tumor during the delivery of chemoradiation therapy (CRT) for pancreatic cancer.Diagnostic-quality CT data acquired daily during routine CT-guided CRT using a CT-on-rails for 20 pancreatic head cancer patients were analyzed. On each daily CT, the pancreatic head, the spinal cord and the aorta were delineated and the histograms of CT number (CTN) in these contours were extracted. Eight histogram-based radiomic metrics including the mean CTN (MCTN), peak position, volume, standard deviation (SD), skewness, kurtosis, energy and entropy were calculated for each fraction. Paired t-test was used to check the significance of the change of specific metric at specific time. GEE model was used to test the association between changes of metrics over time for different pathology responses.In general, CTN histogram in the pancreatic head (but not in spinal cord) changed during the CRT delivery. Changes from the 1st to the 26th fraction in MCTN ranged from -15.8 to 3.9 HU with an average of -4.7 HU (p<0.001). Meanwhile the volume decreased, the skewness increased (less skewed), and the kurtosis decreased (less peaked). The changes of MCTN, volume, skewness, and kurtosis became significant after two weeks of treatment. Patient pathological response is associated with the changes of MCTN, SD, and skewness. In cases of good response, patients tend to have large reductions in MCTN and skewness, and large increases in SD and kurtosis.Significant changes in CT radiomic features, such as the MCTN, skewness, and kurtosis in tumor were observed during the course of CRT for pancreas cancer based on quantitative analysis of daily CTs. These changes may be potentially used for early assessment of treatment response and stratification for therapeutic intensification
Patient characteristics, treatment methods and outcome data.
<p>Patient characteristics, treatment methods and outcome data.</p
The correlation of the change and initial value of MCTN.
<p>The straight line is the best linear fit.</p
Comparisons of the average changes of for the good- and poor-response groups.
<p>(a) moments of histogram including MCTN, SD, skewness, kurtosis, and (b) volume in GTV from the first RT fraction during the course of CRT. The error bar is the standard error of the values of the cohort.</p