ID stringlengths 30 33 | messages stringlengths 441 17.8k | chosen_analysis stringlengths 10 7.4k | chosen_final stringlengths 1 18.2k | rejected_analysis stringlengths 10 11k | rejected_final stringlengths 1 63.8k |
|---|---|---|---|---|---|
daring_anteater_en-76596-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2030-11-30", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to respond maybe with solving inequality? Probably give solution set description. | この不等式は **\(x\) と \(y\) の和が 6 以上** であることを意味しています。
解く手順と結果は次の通りです。
---
## 1️⃣ 不等式の形を整理する
\[
x+y \ge 6
\]
左辺は二つの変数の線形結合なので、平面上で直線 \(x+y=6\) を境界にした領域になります。
---
## 2️⃣ 境界線（等号の場合）
\[
x+y = 6 \quad\Longrightarrow\quad y = -x+6
\]
これは座標平面で傾き \(-1\)、切片が \((0,6)\) および \((6,0)\) にある直線です。
---
## 3️⃣ 領域の決定
「≥」なので... | We need to respond? Probably solve inequality? Provide solution set description. | \(x+y\ge 6\) は、平面上の直線 \(x+y=6\) を境界として、その上側（左上）にあるすべての点 \((x,y)\) の集合です。
### グラフでの表し方
1. **境界線**
- 方程式 \(x+y=6\) は切片が \((6,0)\) と \((0,6)\) の直線です。
- 境界線は「≥」なので実線で描きます。
2. **領域の決定**
- 任意の点を代入して判定します。例えば原点 \((0,0)\) を代入すると
\[
0+0 = 0 < 6
\]
となり不等式を満たさないので、原点側は除外します。
- したがって、直線の... |
daring_anteater_en-60418-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2024-09-03", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need answer. | **Answer:** The first practical heat‑engine was built by **Thomas Savery** in England (patented in 1698), and it is generally regarded as the world’s earliest “heat engine” that could do useful work.
Below is a concise timeline showing how early attempts at turning heat into mechanical energy led up to Savery’s inve... | Need answer. | The very first practical **heat‑engine** was built in the early 19th century by **Sir George Stevenson (1781–1848)**, an English engineer and inventor.
### Why Stevenson is credited with the “first” heat engine
| Year | Development | Significance |
|------|-------------|--------------|
| **1815** | Stevenson patente... |
daring_anteater_en-8424-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2023-11-11", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to solve but no specific congruences given? Probably missing? Maybe they expect general method? Might be trick: "Solve the following system of congruences:" without listing—maybe answer: use Chinese remainder theorem. Provide explanation. | **Answer – How to solve a system of linear congruences**
When you are asked to “solve the following system of congruences’’ the usual tool is the **Chinese Remainder Theorem (CRT)**.
Below is a step‑by‑step recipe that works for any collection of congruences of the form
\[
\begin{cases}
x \equiv a_{1}\pmod {n_{1}... | We need to solve? Probably find x satisfying given system but not provided. Maybe missing statement. Assume typical Chinese remainder example? Can't answer. | **解答**
与えられた同値式系
\[
\begin{cases}
x \equiv a_{1}\pmod{m_{1}}\\[2pt]
x \equiv a_{2}\pmod{m_{2}}\\[2pt]
\;\vdots \\[2pt]
x \equiv a_{k}\pmod{m_{k}}
\end{cases}
\qquad (m_i>0)
\]
について、すべての解 \(x\) を求めます。以下では **中国剰余定理（CRT）** を用いた一般的な手順を示し、途中で具体例も併せて説明します。
---
## 1. 前提条件：互いに素であることの確認
CRT が直接適用できるのは、全てのモジュラスが互いに素（＝任意の... |
daring_anteater_en-8424-turn1--1 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2023-11-11", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to solve but no specific congruences given? Probably missing? Maybe they expect general method? Might be trick: "Solve the following system of congruences:" without listing—maybe answer: use Chinese remainder theorem. Provide explanation. | **Answer – How to solve a system of linear congruences**
When you are asked to “solve the following system of congruences’’ the usual tool is the **Chinese Remainder Theorem (CRT)**.
Below is a step‑by‑step recipe that works for any collection of congruences of the form
\[
\begin{cases}
x \equiv a_{1}\pmod {n_{1}... | We need to solve? Probably Chinese remainder problem but not given numbers yet. Missing statement. Maybe they expect general method? Might be incomplete. Assume typical example: x ≡ 2 mod3, x≡3 mod5, x≡2 mod7 => solution 23 mod105. Provide explanation. | **Solution – step‑by‑step**
Below is a complete walk‑through of how one solves a *system of linear congruences* using the **Chinese Remainder Theorem (CRT)**.
The same procedure works for any set of pairwise‑coprime moduli; after the exposition I will apply it to the concrete system that usually appears in textbooks... |
daring_anteater_en-10235-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2025-07-16", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to respond maybe solving? Probably they want solution expression y in terms of x etc. Provide steps. | この一次方程式は
\[
x-3y=1
\]
です。
**目的に応じて次の形で解くことができます。**
---
## 1. \(y\) を求める（\(y\) を \(x\) の関数にする）
\[
\begin{aligned}
x-3y &= 1 \\
-3y &= 1-x \\
y &= \frac{x-1}{3}\;(\text{符号を変えて})
\end{aligned}
\]
したがって
\[
\boxed{\,y=\dfrac{x-1}{3}\,}
\]
---
## 2. \(x\) を求める（\(x\) を \(y\) の関数にする）
\[
\begin{aligned}
x-3y &=... | We need to respond? Probably solve equation? It's linear relation between x and y. Provide expression for x in terms of y or graph. I'll answer. | **方程式**
\[
x-3y=1
\]
は、\(x\) と \(y\) の一次（線形）関係です。
---
## 1. \(x\) を \(y\) の関数として解く
\[
\begin{aligned}
x-3y &= 1 \\
x &= 1+3y .
\end{aligned}
\]
したがって、任意の実数 \(y\) に対して
\[
\boxed{x = 1 + 3y}
\]
が成り立ちます。
---
## 2. \(y\) を \(x\) の関数として解く
\[
\begin{aligned}
x-3y &= 1 \\
-3y &= 1-x \\
y &= \frac{x-1}{3}.
\end{a... |
daring_anteater_en-74442-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2022-01-09", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We just output solution? Probably solve equation? It's one linear equation in three variables; express e.g., x = 1+2y -z. Provide param form. | この式は 3 つの変数 \(x,\;y,\;z\) を含む一次方程式ですから、
**1 本の独立した制約しか持たない**ことになります。したがって解空間は平面（2 次元）で、自由変数を 2 つ取って残りの変数を表す形に書き直すことができます。
---
## 標準形への変形
\[
x - 2y + z = 1
\]
両辺から \(\displaystyle -2y+z\) を移項して \(x\) を求めます。
\[
\boxed{\,x = 1 + 2y - z\,}
\]
ここで **\(y\)** と **\(z\)** は任意の実数です（パラメータ）。
---
## パラメトリック表現
たとえば次のように置... | We need to respond? Probably solve equation? It's one linear equation with three variables; express solution parametric. Provide answer. | この式は **一次方程式**（線形方程式）で、変数が \(x,\;y,\;z\) の３つあります。
したがって、１つだけでは解は一意に決まりません。自由変数を２つ取ってパラメータ表示にすれば、すべての解集合を書き下すことができます。
---
## 一般解（パラメトリック形式）
\[
x-2y+z=1 \qquad (1)
\]
### 手順
1. 任意に 2 つの変数を「自由変数」として選ぶ。たとえば
\[
y=s,\qquad z=t \quad (s,t\in\mathbb{R})
\]
2. 式 (1) を \(x\) について解く。
\[
x = 1 + 2y - z = 1 +... |
daring_anteater_en-39963-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2025-12-18", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need to answer with steps, formulas, tips. | ## ビジネスがキャッシュフローを「計算」し、「改善」するための実践的ロードマップ
以下は、**中小企業から成長段階のベンチャーまで**どんな規模でも適用できる手順です。
各ステップには **具体的な計算式・ツール例・チェックリスト** を添えているので、すぐにスプレッドシートや会計ソフトへ落とし込めます。
---
### 1️⃣ キャッシュフローの基本構造を把握する
| 区分 | 主な項目 | 正味効果（＋＝増える／－＝減る） |
|------|----------|-----------------------------------|
| **営業活動 (Operating Cash Flow)** | ・... | Need to explain calculation and improvement steps. Provide formulas, examples, tips. | ## 1️⃣ キャッシュ・フローとは何か？
| 用語 | 意味 |
|------|------|
| **営業活動によるキャッシュ・フロー (CFO)** | 商品販売やサービス提供など、本業で生み出した現金の流入‑流出。 |
| **投資活動によるキャッシュ・フロー (CFI)** | 設備投資、土地建物の取得・売却、長期有価証券の取引等。 |
| **財務活動によるキャッシュ・フロー (CFF)** | 借入・返済、株式発行・配当支払いなど資本構成に関わる現金の動き。 |
| **期末キャッシュ残高** | 前期残高 + CFO + CFI + CFF |
> **ポイント**：利益（損益計算書）とキャッシュ・フローは別... |
daring_anteater_en-8221-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2023-04-25", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need to answer in English likely. Provide causes (colonial policy, communal tensions, etc.) and consequences (refugee crisis, wars, Kashmir dispute). | **The Partition of British India (1947) – Causes and Consequences**
---
## I. WHY THE PARTITION HAPPENED
| Category | Main Factors | How They Operated |
|----------|--------------|-------------------|
| **Colonial administrative decisions** | • *“Divide‑and‑rule”* strategy of the British Raj.<br>• Creation of sepa... | Need essay style answer. | ## はじめに
1947 年のインド・パキスタン分離独立（**Partition**)は、南アジア史上最大級の歴史転換点であり、政治・社会・経済・文化のすべてに深い爪痕を残しました。
本稿では、以下の二つの視点から整理します。
1. **原因（Causes）** – 何故分割という極端な選択肢に至ったのか。
2. **結果（Consequences）** – 分割がもたらした直接的・間接的影響と、現在まで続く遺産。
---
## Ⅰ．分割の主な原因
| カテゴリ | 主な要因 | 内容・背景 |
|----------|----------|------------|
| **植民地支配の構造** | ・英... |
daring_anteater_en-70671-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2028-08-02", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need to list industries and example companies using edge computing. Provide categories. | ## Edge‑Computing Adoption by Industry
Below is a quick reference guide that groups major players (both hardware‑/software‑focused firms and end‑users) by **industry sector**. For each sector I’ve highlighted:
| Industry | Why Edge Computing matters there | Representative Companies (end‑users & tech providers) |
|--... | Need list by industry examples. Provide table. | Below is a quick‑reference guide that groups well‑known **edge‑computing adopters** by major industry sectors.
For each company I’ve added a one‑sentence note on *why* they need edge (or how they use it) and a link to a public source (press release, case study, or blog post) so you can dig deeper.
| Industry | Compa... |
daring_anteater_en-6586-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2023-02-08", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to answer algorithmically? Probably describe minimal enclosing circle (smallest enclosing circle). Provide method like Welzl's algorithm O(n) expected. Explain steps. | **最小外接円（Smallest Enclosing Circle / Minimum‑Covering Disk）**
与えられた **凸多角形** のすべての頂点を含み、半径が最小になる円を求める問題です。
凸性は必ずしも必要ありませんが，頂点だけが分かっていれば同じ手順で解けます。
---
## 1. 基本概念と性質
| 用語 | 説明 |
|------|------|
| **最小外接円 (MEC)** | 多角形の全点を覆う半径最小の円。 |
| **境界点 (support points)** | MEC の境界上にある入力点の集合。最大でも 3 個しか存在しないことが知られています。（0 個＝空集合... | We need to explain minimal enclosing circle algorithm. Provide steps and maybe code. | ## 最小外接円（Smallest Enclosing Circle / Minimum Covering Circle）
**問題**
平面上に与えられた凸多角形 \(P\)（頂点は反時計回りに並んでいると仮定）をすべて含む、半径が最小になる円を求めよ。
---
### 1️⃣ 基本的な性質
* 任意の点集合の **最小包囲円**（MMC：Minimum‑Enclosing‑Circle）は次のいずれかで構成されます
1. **2 点で決定** – 円の径はそれら2点間の距離の半分。
2. **3 点で決定** – その3点が円周上にあり、円は唯一の外接円です。
* したがって、凸多角形の場合... |
daring_anteater_en-12435-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2027-12-19", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need third proportional: if a:b = b:c then c is third proportional to a and b? Actually given two numbers a=6, b=18, find x such that 6 : 18 :: 18 : x => x = (18*18)/6 = 54. So answer 54. | The “third proportional’’ \(x\) to the numbers \(a\) and \(b\) satisfies
\[
\frac{a}{b}= \frac{b}{x}.
\]
Here \(a=6\) and \(b=18\). Solving for \(x\):
\[
\frac{6}{18}= \frac{18}{x}
\quad\Longrightarrow\quad
6x = 18 \times 18
\quad\Longrightarrow\quad
x = \frac{18^2}{6}= \frac{324}{6}=54.
\]
\[
\boxed{54}
\] | We need ratio 6:? = ?:18 so x such that 6/x = x/18 => x^2=108 => x=√108=6√3≈10.392. Probably answer 6√3. | The “third proportional” \(x\) is defined by
\[
\frac{6}{x}= \frac{x}{18}.
\]
Cross‑multiplying gives
\[
6\cdot 18 = x^{2}\quad\Longrightarrow\quad x^{2}=108.
\]
Hence
\[
x=\sqrt{108}= \sqrt{36\cdot 3}=6\sqrt{3}\approx 10.39 .
\]
So the third proportional to the numbers \(6\) and \(18\) is **\(6\sqrt{3}\)**... |
daring_anteater_en-6980-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2030-09-24", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need to explain steps. | **Long‑division of \(999 \div 100\)**
We want the quotient (the number we get) and any remainder when dividing 999 by 100.
---
### Step‑by‑step layout
```
_______
100 ) 999
```
1️⃣ **How many times does 100 go into the first part of the dividend?**
The divisor is 3 digits (100), so we look at the l... | We need to show steps. | **Long‑division of \(999\) ÷ \(100\)**
```
_______
100 ) 999
```
1️⃣ **How many times does 100 go into the first digit (9)?**
It doesn’t – 100 is larger than 9, so we look at the first two digits together (99).
2️⃣ **How many times does 100 go into 99?**
Again it’s still too big, because 100 > 99.
T... |
daring_anteater_en-53869-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2026-12-13", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need to output code and explanation. | **What is a transition (stochastic) matrix?**
A square \(n\times n\) matrix \(P\) is a *valid transition matrix* (also called a stochastic or probability‑transition matrix) when it satisfies two conditions
| Condition | Meaning |
|-----------|---------|
| **Non‑negative entries** | Every element \(p_{ij}\ge 0\). P... | Need to output code maybe in Python with explanation. | ## 概要
遷移行列（transition matrix）とは、**確率的マルコフ連鎖**で使われる **行‐和が 1 になる非負実数行列**です。
典型的には次の条件を満たす必要があります。
| 条件 | 内容 |
|------|------|
| 1️⃣ | 要素は **0 以上**（負の確率はあり得ない）。 |
| 2️⃣ | 各 **行の総和がちょうど 1**（状態 i から出るすべての遷移確率の合計＝1）。<br>※「列」ごとに正規化された *左* 確率分布を用いる場合は「列の総和 = 1」でも構いませんが、ここでは **行正規化** を前提にします。 |
| 3️⃣ | （オプション）浮動小数点誤... |
daring_anteater_en-41152-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2025-02-26", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to output code with explanation. | **Python – ROT‑13 デコーダ**
`ROT13` はアルファベットを 13 文字ずらすシーザー暗号です。
大文字・小文字をそれぞれ独立に回転させ、英字以外（数字・記号・空白など）はそのまま出力します。
以下のスクリプトは
* 標準入力からエンコードされた文字列を受け取る（または `rot13_decode()` 関数に直接渡す）
* デコードした結果を標準出力へ書く
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
from typing import Iterable
def rot13_char(ch:... | We need to output code with explanation. | **ROT13 デコーダー（Python）**
以下のスクリプトは、標準入力またはコマンドライン引数で渡された文字列を **ROT13** (Caesar cipher のシフト 13) で復号し、結果を出力します。
文字コードは ASCII/Unicode の英字のみを対象とし、大文字・小文字をそれぞれ正しく変換します。数字や記号、空白などはそのまま残します。
```python
#!/usr/bin/env python3
"""
rot13_decoder.py - ROT13 エンコードされたテキストをデコードするユーティリティ。
使い方:
$ echo "Gur dhvpx oebja sbk whzcf... |
daring_anteater_en-2887-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2025-01-28", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | Need answer with strategies. | ## はじめに
ヘイトクライムやテロは、**「相手を人間として認識できない」**という根底にある感情・思考が大きく関わっています。
したがって **「共感（エンパシー）」と「理解（コンテクストの把握）」を育むこと** が、長期的な予防策として最も効果的です。以下では、個人レベルから制度・社会レベルまで、実践しやすい具体策を階層別にまとめます。
---
## 1️⃣ 個人・コミュニティレベルのアプローチ
| カテゴリ | 具体的施策 | 期待される効果 |
|----------|-----------|----------------|
| **対話の場を設ける** | - 「ストーリーテリング・サロン」：異なる... | Need answer with suggestions. | ## はじめに
憎悪犯罪（ヘイトクライム）やテロは、**「相手への共感が欠如した状態」**から生まれやすいと言われています。
個人が他者の感情・経験に心を寄せられないと、差別的ステレオタイプが固定化し、暴力へとエスカレートします。したがって **“共感と理解” を育むことは予防策の根幹**です。以下では、教育・コミュニティ・メディア・政策という四つの柱に分けて、実践可能な具体策を示します。
---
## 1️⃣ 教育現場での共感トレーニング
| 手段 | 内容 | 実装ポイント |
|------|------|----------------|
| **感情リテラシー授業** | 「自分・相手の感情を名前で言い表す... |
daring_anteater_en-83874-turn1--0 | [{"role": "system", "name": null, "content": [{"model_identity": "You are LLM-jp-4, a large language model trained by LLM-jp.", "reasoning_effort": "Low", "conversation_start_date": "2028-11-25", "knowledge_cutoff": "2025-12", "channel_config": {"valid_channels": ["analysis", "commentary", "final"], "channel_required":... | We need to output code solution description. Probably in Python. Provide algorithm using prime factorization and backtracking to minimize number given divisor count. Use sorted primes list up to reasonable limit (since result grows fast). We'll explain. | **Solution Explanation**
For an integer
\[
x = p_1^{e_1}\;p_2^{e_2}\;\dots\;p_k^{e_k}
\]
where `p₁ < p₂ < …` are distinct primes and `e_i ≥ 1`, the number of its **positive divisors** is
\[
d(x)= (e_1+1)(e_2+1)\dots(e_k+1)
\]
So we have to choose a multiset of exponents `{e_i}` such that their “shifted” produc... | We need to output code solution description. Probably in Python. Provide algorithm using prime factorization search via DFS/BFS. Explain complexity. | **Solution Explanation**
For a positive integer
```
x = p1^e1 · p2^e2 · … · pk^ek (pi are distinct primes, ei ≥ 1)
```
the number of its positive divisors is
```
d(x) = (e1+1)(e2+1)…(ek+1)
```
So, to obtain a given divisor‑count `N` we have to choose a multiset of
exponents `{ei}` whose “augmented” pr... |
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llm-jp-4-33b-thinking-dpo-data
Overview
This dataset is a Direct Preference Optimization (DPO) dataset used to train llm-jp-4-33b-thinking.
It is constructed by pairing multiple candidate responses for a given prompt and selecting preferred (chosen) and non-preferred (rejected) responses. The splits reasoning_low, reasoning_medium, and reasoning_high correspond to different reasoning effort settings used during response generation.
The fields chosen_analysis, chosen_final, rejected_analysis, and rejected_final are generated using a model trained via supervised fine-tuning (SFT). These responses are then evaluated by gpt-oss-120b, which determines the preferred (chosen) and non-preferred (rejected) responses.
To support the continued development of LLM-jp, we would greatly appreciate it if you could share how you utilize LLM-jp outcomes via the survey form.
Data Sources and Licenses
The DPO dataset for llm-jp-4-33b-thinking is constructed from the following data sources.
Since each dataset has its own license, please ensure compliance with the respective licenses when using this dataset.
Some subsets cannot be redistributed due to licensing restrictions and are therefore not included.
| Dataset | License | Notes |
|---|---|---|
| ac_self_inst | answer-carefully-dataset-tou | Not included due to redistribution restrictions |
| daring_anteater | CC BY 4.0 | - |
| flan | ODC-BY | - |
| ichikara | - | Requires paid license, not included |
| jaster_v1.4.1 | CC BY-SA 4.0, CC BY-SA 3.0, CC BY 4.0, BSD-3, Apache-2.0, MIT | - |
| llmjp_extraction_wiki_ja_v0.x | Apache-2.0 | - |
| llmjp_magpie_sft_v1.0 | Apache-2.0 | - |
| llmc_math_dataset | - | To be released |
| logical_math_coding_wizard8x22b | Apache-2.0 | - |
| multiturn_calm3 | Apache-2.0, CC BY-SA 3.0, CC0, CC BY 4.0 | - |
| nemotron_post_v2_stem | CC BY 4.0 | - |
| nemotron_post_v3_chat | CC BY 4.0 | - |
| nemotron_post_v3_if | CC BY 4.0 | - |
| nemotron_post_v3_math | CC BY-SA 4.0 | - |
| random_to_fixed_multiturn_calm3 | Apache-2.0 | - |
| synthetic_if_ja | - | To be released |
| synthetic_jp_en_coding | Apache-2.0 | - |
Data Format
Each sample has the following format:
{
"ID": "...",
"messages": [
{"role": "system", "name": null, "content": ["..."]},
{"role": "user", "name": null, "content": ["..."]},
],
"chosen_analysis": "...",
"chosen_final": "...",
"rejected_analysis": "...",
"rejected_final": "..."
}
chosen_analysisrepresents the reasoning process (chain-of-thought) of the preferred responsechosen_finalrepresents the final response of the preferred responserejected_analysisrepresents the reasoning process of the non-preferred responserejected_finalrepresents the final response of the non-preferred response
The messages field contains the input conversation (prompt context).
This structure allows training preference models that compare two candidate responses conditioned on the same prompt.
Notes
- This dataset contains responses generated by a large language model.
- The preference labels (chosen vs rejected) are constructed automatically and may contain noise.
- Generated content may include errors, inaccuracies, biases, or harmful outputs.
Send Questions to
llm-jp(at)nii.ac.jp
Model Card Authors
The names are listed in alphabetical order.
Hirokazu Kiyomaru and Takashi Kodama.
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