医療LLMの研究紹介 助田一晟 松尾研LLMコミュニティ【Paper & Hacks Vol.26】参加記録 AI(14)

LLM(Large Language Model) Calendar 2024
https://qiita.com/advent-calendar/2024/llm
Day 4 投稿予定記事です。

松尾研LLMコミュニティ【Paper & Hacks Vol.26】 医療LLMの研究紹介
https://matsuolab-community.connpass.com/event/336858/

発表者: 松尾研LLMコミュニティメンバー 助田一晟（EQUES CTO / 東大 / 松尾研大規模言語モデル講座 第11回講師）

Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resources
https://arxiv.org/abs/2409.11783

From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
https://arxiv.org/pdf/2411.03590v1

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# 2411.03590v1 word count

|term |count|
|:----|:----|
| |445|
|the|222|
|and|184|
|of|160|
|in|128|
|to|121|
|for|94|
|models|89|
|on|85|
|a|84|
|o|81|
|with|72|
|model|70|
|reasoning|70|
| |69|
|performance|63|
|arxiv|56|
|preview|56|
|gpt|53|
|that|51|
|medical|49|
|as|44|
|is|44|
|prompting|42|
|we|41|
|medprompt|39|
|by|35|
|language|35|
|shot|34|
|can|33|
|this|31|
|time|31|
|more|29|
|preprint|28|
|strategies|28|
|tokens|26|
|are|25|
|benchmarks|25|
|from|25|
|results|25|
|run|25|
|these|25|
|accuracy|24|
|few|24|
|llms|24|
|b|23|
|ensembling|23|
|multiple|22|
|cost|21|
|inference|21|
|prompt|21|
|such|21|
|across|20|
|at|20|
|capabilities|20|
|or|20|
|large|19|
|o|19|
|of|19|
|learning|18|
|medqa|18|
|eric|17|
|further|17|
|like|17|
|ope|17|
|our|16|
|techniques|16|
|using|16|
|be|15|
|chain|15|
|figure|15|
|information|15|
|questions|15|
|research|15|
|tasks|15|
|an|14|
|examples|14|
|has|14|
|new|14|
|nlz|14|
|question|14|
|step|14|
|which|14|
|have|12|
|it|12|
|knowledge|12|
|methods|12|
|question|12|
|resources|12|
|their|12|
|use|12|
|窶|12|
|a|11|
|challenge|11|
|context|11|
|dataset|11|
|each|11|
|horvitz|11|
|its|11|
|self|11|
|steering|11|
|thought|11|
|was|11|
|when|11|
|while|11|
|al|10|
|answerchoices|10|
|approach|10|
|benchmark|10|
|computational|10|
|et|10|
|exam|10|
|generation|10|
|leveraging|10|
|li|10|
|llm|10|
|multi|10|
|openai|10|
|problem|10|
|problems|10|
|shot|10|
|specialized|10|
|training|10|
|zhang|10|
|advanced|9|
|also|9|
|area|9|
|domain|9|
|final|9|
|not|9|
|processing|9|
|sampling|9|
|study|9|
|systems|9|
|token|9|
|tools|9|
|without|9|
|wu|9|
|advances|8|
|api|8|
|choice|8|
|context|8|
|datasets|8|
|dynamic|8|
|effective|8|
|enhance|8|
|even|8|
|external|8|
|general|8|
|generating|8|
|improve|8|
|into|8|
|med|8|
|neural|8|
|output|8|
|outputs|8|
|real|8|
|remains|8|
|set|8|
|technique|8|
|the|8|
|wang|8|
| |8|


# 2409.11783v2 word count

|term|count|
|:----|:----|
|the|289|
| |196|
|in|185|
|and|154|
|b|144|
|of|140|
|medical|115|
|to|112|
|is|100|
|a|96|
|llama|88|
|for|76|
|models|72|
|japanese|65|
|on|65|
|we|59|
|model|56|
|arxiv|52|
|as|52|
|llms|51|
| |48|
|by|46|
|language|45|
|are|43|
|our|43|
|english|42|
|with|42|
|v|41|
|that|39|
|ja|37|
|llm|35|
|training|34|
|from|32|
|tuning|31|
|performance|30|
|this|30|
|which|29|
|be|28|
|dataset|28|
|not|28|
|en|26|
|preprint|26|
|base|25|
|evaluation|25|
|fine|25|
|parameter|25|
|benchmarks|24|
|it|24|
|the|24|
|qwen|23|
|table|23|
|wang|23|
|accuracy|22|
|al|22|
|data|22|
|large|22|
|open|22|
|竏|22|
|igakuqa|21|
|jmedllm|21|
|llama|21|
|zhang|21|
|prompt|20|
|shot|20|
|can|19|
|medswallow|19|
|other|19|
|swallow|19|
|et|18|
|https|18|
|clinical|17|
|have|17|
|in|17|
|medmcqa|17|
|mfpt|17|
|mpeft|17|
|or|17|
|preferred|17|
|question|17|
|source|17|
|based|16|
|benchmark|16|
|each|16|
|instruction|16|
|medqa|16|
|however|15|
|huggingface|15|
|instruct|15|
|samples|15|
|also|14|
|centric|14|
|co|14|
|experiments|14|
|input|14|
|med|14|
|only|14|
|ours|14|
|scores|14|
|while|14|
|answering|13|
|li|13|
|resources|13|
|usmle|13|
|was|13|
|and|12|
|been|12|
|choice|12|
|domain|12|
|inference|12|
|openbiollm|12|
|their|12|
|these|12|
|using|12|
|an|11|
|computational|11|
|few|11|
|five|11|
|following|11|
|has|11|
|of|11|
|significant|11|
|to|11|
|use|11|
|used|11|
|at|10|
|both|10|
|chen|10|
|developed|10|
|languages|10|
|medicine|10|
|more|10|
|number|10|
|prompting|10|
|score|10|
|settings|10|
|tasks|10|
|tokens|10|
|adaptation|9|
|com|9|
|compared|9|
|conference|9|
|corpus|9|
|full|9|
|generally|9|
|improvements|9|
|information|9|
|jmmlu|9|
|knowledge|9|
|method|9|
|mmlu|9|
|one|9|
|practical|9|
|research|9|
|sukeda|9|
|task|9|
|text|9|
|two|9|
|wei|9|
|窶|9|
|available|8|
|cot|8|
|e|8|
|for|8|
|further|8|
|hand|8|
|improvement|8|
|its|8|
|jp|8|
|medical|8|
|palm|8|
|parameters|8|
|questions|8|
|response|8|
|results|8|
|series|8|
|specific|8|
|specifically|8|
|studies|8|
|translated|8|
|work|8|
| |8|

```macOS:bash
$ docker run -v /Users/ogawakiyoshi/llm:/tmp/llm -it kaizenjapan/llm  /bin/bash

```

```docker:bash
# tr 'A-Z' 'a-z' < 2411.03590v1.txt > 2411.03590v1s.txt
# awk -f wc.awk 2411.03590v1s.txt > 2411.03590v1.wc
# tr 'A-Z' 'a-z' < 2409.11783v2.txt > 2409.11783v2s.txt
# awk -f wc.awk 2409.11783v2s.txt  > 2409.11783v2.wc
```


```macOS:bash
$ docker commit c6430640806f  kaizenjapan/llm  
$  docker push   kaizenjapan/llm  
```