ABOUT_TEXT = """
We compute the win percentage for a reward model on hand curated chosen-rejected pairs for each prompt.
A win is when the score for the chosen response is higher than the score for the rejected response.

Note: Models with (*) after the model name are independently submitted model scores which have not been verified by the RewardBench team.

## Overview

We average over 4 core sections (per prompt weighting):
1. **Chat**: Includes the easy chat subsets (alpacaeval-easy, alpacaeval-length, alpacaeval-hard, mt-bench-easy, mt-bench-medium)
2. **Chat Hard**: Includes the hard chat subsets (mt-bench-hard, llmbar-natural, llmbar-adver-neighbor, llmbar-adver-GPTInst, llmbar-adver-GPTOut, llmbar-adver-manual)
3. **Safety**: Includes the safety subsets (refusals-dangerous, refusals-offensive, xstest-should-refuse, xstest-should-respond, do not answer)
4. **Reasoning**: Includes the code and math subsets (math-prm, hep-cpp, hep-go, hep-java, hep-js, hep-python, hep-rust)

For Reasoning, we increase the weight of the PRM-Math subset so code and math abilities are weighed equally in the final number, rather than increasing the relevance of code.
We add a final column, **Prior Sets** -- includes the test sets ([anthropic_helpful](https://huggingface.co/datasets/Anthropic/hh-rlhf), [anthropic_hhh](https://huggingface.co/datasets/HuggingFaceH4/hhh_alignment), [shp](https://huggingface.co/datasets/stanfordnlp/SHP), [summarize](https://huggingface.co/datasets/openai/summarize_from_feedback))
Prior sets is weighted 0.5x in the final score to avoid gamification by training on the available training sets of Anthropic HH, SHP, and Summarize.

Once all subsets weighted averages are achieved, the final RewardBench score is the average across the 5 subset scores.


We include multiple types of reward models in this evaluation:
1. **Sequence Classifiers** (Seq. Classifier): A model, normally trained with HuggingFace AutoModelForSequenceClassification, that takes in a prompt and a response and outputs a score.
2. **Custom Classifiers**: Research models with different architectures and training objectives to either take in two inputs at once or generate scores differently (e.g. PairRM and Stanford SteamSHP).
3. **DPO**: Models trained with Direct Preference Optimization (DPO), with modifiers such as `-ref-free` or `-norm` changing how scores are computed. *Note*: This also includes other models trained with implicit rewards, such as those trained with [KTO](https://arxiv.org/abs/2402.01306). 
4. **Random**: Random choice baseline.
4. **Generative**: Prompting fine-tuned models to choose between two answers, similar to MT Bench and AlpacaEval.

All models are evaluated in fp16 expect for Starling-7B, which is evaluated in fp32.
*Note*: The reference models for DPO models (and other implicit rewards) can be found in two ways.
* Click on a specific model in results and you'll see a key `ref_model`, e.g. [Qwen](https://huggingface.co/datasets/allenai/reward-bench-results/blob/main/eval-set/Qwen/Qwen1.5-72B-Chat.json).
* All the reference models are listed in the [evaluation configs](https://github.com/allenai/reward-bench/blob/main/scripts/configs/eval_configs.yaml).


### Subset Details

Total number of the prompts is: 2985, filtered from 5123.

| Subset             | Num. Samples (Pre-filtering, post-filtering) | Description |
| :---------- | :-----: | :---------: |
| alpacaeval-easy    | 805, 100          | Great model vs poor model            |
| alpacaeval-length    | 805, 95          | Good model vs low model, equal length            |
| alpacaeval-hard    | 805, 95          | Great model vs baseline model            |
| mt-bench-easy      | 28, 28           | MT Bench 10s vs 1s            |
| mt-bench-medium    | 45, 40           | MT Bench 9s vs 2-5s            |
| mt-bench-hard      | 45, 37          | MT Bench 7-8 vs 5-6            |
| refusals-dangerous | 505, 100          | Dangerous response vs no response            |
| refusals-offensive | 704, 100          | Offensive response vs no response            |
| llmbar-natural     | 100          | (See [paper](https://arxiv.org/abs/2310.07641)) Manually curated instruction pairs |
| llmbar-adver-neighbor | 134          | (See [paper](https://arxiv.org/abs/2310.07641)) Instruction response vs. off-topic prompt response |
| llmbar-adver-GPTInst | 92          | (See [paper](https://arxiv.org/abs/2310.07641)) Instruction response vs. GPT4 generated off-topic prompt response |
| llmbar-adver-GPTOut |  47          | (See [paper](https://arxiv.org/abs/2310.07641)) Instruction response vs. unhelpful-prompted GPT4 responses |
| llmbar-adver-manual |  46          | (See [paper](https://arxiv.org/abs/2310.07641)) Challenge set chosen vs. rejected |
| xstest-should-refuse | 450, 154         | False response dataset (see [paper](https://arxiv.org/abs/2308.01263))        |
| xstest-should-respond | 450, 250         | False refusal dataset (see [paper](https://arxiv.org/abs/2308.01263))        |
| do not answer | 939, 136         | [Prompts which responsible LLMs do not answer](https://huggingface.co/datasets/LibrAI/do-not-answer)        |
| math-prm | 447         | Human references vs. model error from OpenAI's Let's Verify Step by Step        |
| hep-cpp | 164         | C++ code revisions (See [dataset](https://huggingface.co/datasets/bigcode/humanevalpack) or [paper](https://arxiv.org/abs/2308.07124))        |
| hep-go | 164         |   Go code       |
| hep-java | 164         |  Java code        |
| hep-js | 164         |    Javascript code        |
| hep-python | 164         |  Python code         |
| hep-rust | 164         |   Rust code        |

Lengths (mean, std. dev.) include the prompt

| subset                | length bias | chosen_chars   | rejected_chars   | chosen_tokens   | rejected_tokens   | chosen_unique_tokens   | rejected_unique_tokens   |
|-----------------------|-------------|----------------|------------------|-----------------|-------------------|------------------------|--------------------------|
| alpacaeval-easy       | True        | 2283 (1138)    | 646 (482)        | 591 (303)       | 167 (139)         | 253 (117)              | 83 (46)                  |
| alpacaeval-hard       | True        | 1590 (769)     | 526 (430)        | 412 (199)       | 137 (117)         | 173 (67)               | 71 (48)                  |
| alpacaeval-length     | Neutral       | 2001 (1137)    | 2127 (1787)      | 511 (283)       | 597 (530)         | 192 (85)               | 189 (99)                 |
| donotanswer           | False       | 755 (722)      | 1389 (695)       | 170 (161)       | 320 (164)         | 104 (82)               | 157 (73)                 |
| hep-cpp               | Neutral     | 709 (341)      | 705 (342)        | 261 (125)       | 259 (125)         | 100 (29)               | 99 (29)                  |
| hep-go                | Neutral     | 738 (361)      | 734 (361)        | 266 (118)       | 265 (118)         | 100 (29)               | 99 (29)                  |
| hep-java              | Neutral     | 821 (393)      | 814 (390)        | 263 (123)       | 261 (122)         | 102 (30)               | 102 (30)                 |
| hep-js                | Neutral     | 677 (341)      | 673 (339)        | 251 (129)       | 250 (128)         | 93 (29)                | 93 (29)                  |
| hep-python            | Neutral     | 618 (301)      | 616 (300)        | 212 (98)        | 211 (98)          | 86 (26)                | 85 (26)                  |
| hep-rust              | Neutral     | 666 (391)      | 660 (391)        | 221 (132)       | 219 (132)         | 95 (29)                | 95 (29)                  |
| llmbar-adver-GPTInst  | False       | 735 (578)      | 1623 (1055)      | 170 (135)       | 377 (245)         | 93 (59)                | 179 (106)                |
| llmbar-adver-GPTOut   | Neutral     | 378 (339)      | 359 (319)        | 96 (81)         | 101 (94)          | 60 (45)                | 55 (41)                  |
| llmbar-adver-manual   | False       | 666 (584)      | 1139 (866)       | 160 (134)       | 264 (194)         | 92 (63)                | 140 (90)                 |
| llmbar-adver-neighbor | False       | 287 (297)      | 712 (749)        | 70 (76)         | 173 (175)         | 43 (31)                | 91 (70)                  |
| llmbar-natural        | Neutral     | 553 (644)      | 530 (597)        | 139 (162)       | 130 (140)         | 75 (71)                | 70 (62)                  |
| mt-bench-easy         | False       | 1563 (720)     | 2129 (1520)      | 377 (159)       | 551 (415)         | 166 (55)               | 116 (62)                 |
| mt-bench-hard         | False       | 1225 (499)     | 1471 (1016)      | 284 (116)       | 349 (234)         | 131 (45)               | 136 (58)                 |
| mt-bench-med          | Neutral       | 1558 (729)     | 1733 (1312)      | 377 (170)       | 410 (311)         | 162 (58)               | 145 (88)                 |
| refusals-dangerous    | False       | 597 (81)       | 1828 (547)       | 131 (20)        | 459 (136)         | 90 (12)                | 211 (50)                 |
| refusals-offensive    | False       | 365 (116)      | 1092 (1146)      | 82 (25)         | 299 (278)         | 64 (15)                | 134 (101)                |
| xstest-should-refuse  | False       | 584 (419)      | 904 (493)        | 129 (89)        | 217 (115)         | 81 (47)                | 116 (53)                 |
| xstest-should-respond | True        | 771 (420)      | 466 (427)        | 189 (105)       | 107 (94)          | 104 (48)               | 67 (48)                  |

For more details, see the [dataset](https://huggingface.co/datasets/allenai/reward-bench).
"""

TOP_TEXT = """
# RewardBench: Evaluating Reward Models
### Evaluating the capabilities, safety, and pitfalls of reward models
[Code](https://github.com/allenai/reward-bench) | [Eval. Dataset](https://huggingface.co/datasets/allenai/reward-bench) | [Prior Test Sets](https://huggingface.co/datasets/allenai/pref-test-sets) | [Results](https://huggingface.co/datasets/allenai/reward-bench-results) | [Paper](https://arxiv.org/abs/2403.13787) | Total models: {} | * Unverified models | ⚠️ Dataset Contamination

⚠️ Many of the top models were trained on unintentionally contaminated, AI-generated data, for more information, see this [gist](https://gist.github.com/natolambert/1aed306000c13e0e8c5bc17c1a5dd300).
"""