akoksal commited on
Commit
171dba5
·
1 Parent(s): d21a7f7

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +73 -1
README.md CHANGED
@@ -1,3 +1,75 @@
1
  ---
2
- license: other
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ language:
3
+ - en
4
+ - multilingual
5
+ - de
6
+ - it
7
+ - es
8
+ - fr
9
+ tags:
10
+ - instruction-tuning
11
+ - text-generation-inference
12
+ - text2text-generation
13
+ widget:
14
+ - text: Write an essay about meditation. [EOI]
15
+ example_title: Essay Generation
16
+ - text: Give me 5 steps to clean my room. [EOI]
17
+ example_title: How-to Instructions
18
+ - text: How are the continents formed? [EOI]
19
+ example_title: Question-Answering
20
+ - text: >-
21
+ Prompt: A man draws a gun in a dark alley and asks for your wallet. You
22
+ begrudgingly obey. He throws it on the ground, shoots it till it screeches,
23
+ and turns to you; 'you are safe now'. Write a story about given prompt.
24
+ [EOI]
25
+ example_title: Story Generation
26
+ - text: >-
27
+ Write directions of a cooking recipe with these ingredients: chicken breast,
28
+ carrots, green peas, celery, butter, onion, flour, salt, black pepper,
29
+ celery seed, chicken broth, milk, unbaked pie crusts [EOI]
30
+ example_title: Recipe Generation
31
+ - text: Escriba una publicación de blog sobre los beneficios de leer libros. [EOI]
32
+ example_title: Spanish Essay Generation
33
+ - text: >-
34
+ Schreiben Sie einen Blogbeitrag über die Vorteile des Lesens von Büchern.
35
+ [EOI]
36
+ example_title: German Essay Generation
37
+ - text: Rédigez un article de blog sur les avantages de la lecture de livres. [EOI]
38
+ example_title: French Essay Generation
39
+ - text: Scrivi un post sul blog sui vantaggi della lettura di libri. [EOI]
40
+ example_title: Italian Essay Generation
41
+ inference:
42
+ parameters:
43
+ top_p: 0.9
44
+ do_sample: true
45
+ max_length: 100
46
  ---
47
+ ## LongForm-OPT-6.7B
48
+ The LongForm dataset is created by leveraging English corpus examples with augmented instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization.
49
+
50
+
51
+ Github Repo: https://github.com/akoksal/LongForm
52
+
53
+ ### For LongForm-OPT models: Use [EOI] to indicate the end of instruction.
54
+
55
+ LongForm-**T5-XL**: https://huggingface.co/akoksal/LongForm-T5-XL
56
+
57
+ LongForm-**OPT-2.7B**: https://huggingface.co/akoksal/LongForm-OPT-2.7B
58
+
59
+
60
+ ## Evaluation
61
+ We provide in-depth evaluation of LongForm models and baselines in the paper. We present the METEOR scores of models in out-of-domain datasets. In all tasks, Recipe Generation (RGen), long-form question answering (ELI5), short story generation (WritingPrompts/WP), LongForm models outperform prior instruction-tuned models.
62
+ | | **All** | **Recipe Generation** | **ELI5** | **Writing Prompts** |
63
+ |-----------------------|---------|-----------------------------------|----------|---------------------|
64
+ | **T0++** | 10.9 | 18.7 | 3.8 | 10.2 |
65
+ | **Tk-Instruct** | 6.3 | 12.9* | 3.6 | 2.4 |
66
+ | **Flan-T5** | 10.6 | 20.9* | 3.5 | 7.4 |
67
+ | **Alpaca-LLaMA-7B** | 14.6 | 19.5 | 12.5 | 11.8 |
68
+ | **OPT-30B** | 11.1 | 18.6 | 12.2 | 2.6 |
69
+ | **[LongForm-T5-XL](https://huggingface.co/akoksal/LongForm-T5-XL)** | 16.3 | 20.2 | 18.3 | 10.6 |
70
+ | **[LongForm-OPT-2.7B](https://huggingface.co/akoksal/LongForm-OPT-2.7B)** | 17.8 | 15.5 | 17.9 | **19.9** |
71
+ | **[LongForm-OPT-6.7B](https://huggingface.co/akoksal/LongForm-OPT-6.7B)** | 17.7 | 16.9 | 17.2 | 19.0 |
72
+ | **LongForm-LLaMA-7B** | **19.7** | **21.7** | **18.6** | 18.9 |
73
+
74
+ ## Limitations
75
+ The LongForm dataset and models mainly focus on long text generation and have limitations regarding structured prediction tasks in NLP. Additionally, we observe that LongForm models may present hallucination problems similar to those found in LLMs.