X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects Article Swipe
Min‐Qian Liu
,
Ying Shen
,
Zhiyang Xu
,
Yixin Cao
,
Eunah Cho
,
Vaibhav Kumar
,
Reza Ghanadan
,
Lifu Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.08788
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.08788
Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e.g., consistency and naturalness) to obtain a comprehensive assessment. However, multi-aspect evaluation remains challenging as it may require the evaluator to generalize to any given evaluation aspect even if it's absent during training. In this paper, we introduce X-Eval, a two-stage instruction tuning framework to evaluate the text in both seen and unseen aspects customized by end users. X-Eval consists of two learning stages: the vanilla instruction tuning stage that improves the model's ability to follow evaluation instructions, and an enhanced instruction tuning stage that exploits the connections between fine-grained evaluation aspects to better assess text quality. To support the training of X-Eval, we collect AspectInstruct, the first instruction tuning dataset tailored for multi-aspect NLG evaluation spanning 27 diverse evaluation aspects with 65 tasks. To enhance task diversity, we devise an augmentation strategy that converts human rating annotations into diverse forms of NLG evaluation tasks, including scoring, comparison, ranking, and Boolean question answering. Extensive experiments across three essential categories of NLG tasks: dialogue generation, summarization, and data-to-text coupled with 21 aspects in meta-evaluation, demonstrate that our X-Eval enables even a lightweight language model to achieve a comparable if not higher correlation with human judgments compared to the state-of-the-art NLG evaluators, such as GPT-4.
Related Topics
Concepts
Automatic summarization
Computer science
Naturalness
Consistency (knowledge bases)
Natural language generation
Ranking (information retrieval)
Artificial intelligence
Task (project management)
Natural language processing
Question answering
Information retrieval
Machine learning
Natural language
Management
Economics
Quantum mechanics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.08788
- https://arxiv.org/pdf/2311.08788
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388748089
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388748089Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.08788Digital Object Identifier
- Title
-
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation AspectsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-15Full publication date if available
- Authors
-
Min‐Qian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, Lifu HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.08788Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.08788Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2311.08788Direct OA link when available
- Concepts
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Automatic summarization, Computer science, Naturalness, Consistency (knowledge bases), Natural language generation, Ranking (information retrieval), Artificial intelligence, Task (project management), Natural language processing, Question answering, Information retrieval, Machine learning, Natural language, Management, Economics, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.devise | 142 |
| abstract_inverted_index.during | 44 |
| abstract_inverted_index.follow | 88 |
| abstract_inverted_index.higher | 202 |
| abstract_inverted_index.obtain | 18 |
| abstract_inverted_index.paper, | 48 |
| abstract_inverted_index.rating | 149 |
| abstract_inverted_index.tasks, | 157 |
| abstract_inverted_index.tasks. | 136 |
| abstract_inverted_index.tasks: | 174 |
| abstract_inverted_index.tuning | 55, 80, 95, 122 |
| abstract_inverted_index.unseen | 65 |
| abstract_inverted_index.users. | 70 |
| abstract_inverted_index.Boolean | 163 |
| abstract_inverted_index.Natural | 0 |
| abstract_inverted_index.X-Eval, | 51, 115 |
| abstract_inverted_index.ability | 86 |
| abstract_inverted_index.achieve | 197 |
| abstract_inverted_index.aspects | 12, 66, 104, 133, 183 |
| abstract_inverted_index.between | 101 |
| abstract_inverted_index.collect | 117 |
| abstract_inverted_index.coupled | 180 |
| abstract_inverted_index.dataset | 123 |
| abstract_inverted_index.diverse | 131, 152 |
| abstract_inverted_index.enables | 190 |
| abstract_inverted_index.enhance | 138 |
| abstract_inverted_index.model's | 85 |
| abstract_inverted_index.remains | 25 |
| abstract_inverted_index.require | 30 |
| abstract_inverted_index.stages: | 76 |
| abstract_inverted_index.support | 111 |
| abstract_inverted_index.vanilla | 78 |
| abstract_inverted_index.various | 11 |
| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.Language | 1 |
| abstract_inverted_index.compared | 207 |
| abstract_inverted_index.consists | 72 |
| abstract_inverted_index.converts | 147 |
| abstract_inverted_index.dialogue | 175 |
| abstract_inverted_index.enhanced | 93 |
| abstract_inverted_index.evaluate | 58 |
| abstract_inverted_index.exploits | 98 |
| abstract_inverted_index.improves | 83 |
| abstract_inverted_index.involves | 5 |
| abstract_inverted_index.language | 194 |
| abstract_inverted_index.learning | 75 |
| abstract_inverted_index.quality. | 109 |
| abstract_inverted_index.question | 164 |
| abstract_inverted_index.ranking, | 161 |
| abstract_inverted_index.scoring, | 159 |
| abstract_inverted_index.spanning | 129 |
| abstract_inverted_index.strategy | 145 |
| abstract_inverted_index.tailored | 124 |
| abstract_inverted_index.training | 113 |
| abstract_inverted_index.Extensive | 166 |
| abstract_inverted_index.essential | 170 |
| abstract_inverted_index.evaluator | 32 |
| abstract_inverted_index.framework | 56 |
| abstract_inverted_index.generated | 8 |
| abstract_inverted_index.including | 158 |
| abstract_inverted_index.introduce | 50 |
| abstract_inverted_index.judgments | 206 |
| abstract_inverted_index.training. | 45 |
| abstract_inverted_index.two-stage | 53 |
| abstract_inverted_index.typically | 4 |
| abstract_inverted_index.Generation | 2 |
| abstract_inverted_index.answering. | 165 |
| abstract_inverted_index.categories | 171 |
| abstract_inverted_index.comparable | 199 |
| abstract_inverted_index.customized | 67 |
| abstract_inverted_index.diversity, | 140 |
| abstract_inverted_index.evaluating | 6 |
| abstract_inverted_index.evaluation | 24, 38, 89, 103, 128, 132, 156 |
| abstract_inverted_index.generalize | 34 |
| abstract_inverted_index.annotations | 150 |
| abstract_inverted_index.assessment. | 21 |
| abstract_inverted_index.challenging | 26 |
| abstract_inverted_index.comparison, | 160 |
| abstract_inverted_index.connections | 100 |
| abstract_inverted_index.consistency | 14 |
| abstract_inverted_index.correlation | 203 |
| abstract_inverted_index.demonstrate | 186 |
| abstract_inverted_index.evaluators, | 212 |
| abstract_inverted_index.experiments | 167 |
| abstract_inverted_index.generation, | 176 |
| abstract_inverted_index.instruction | 54, 79, 94, 121 |
| abstract_inverted_index.lightweight | 193 |
| abstract_inverted_index.augmentation | 144 |
| abstract_inverted_index.data-to-text | 179 |
| abstract_inverted_index.fine-grained | 102 |
| abstract_inverted_index.multi-aspect | 23, 126 |
| abstract_inverted_index.naturalness) | 16 |
| abstract_inverted_index.comprehensive | 20 |
| abstract_inverted_index.instructions, | 90 |
| abstract_inverted_index.summarization, | 177 |
| abstract_inverted_index.AspectInstruct, | 118 |
| abstract_inverted_index.meta-evaluation, | 185 |
| abstract_inverted_index.state-of-the-art | 210 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |