| Challenge: | Existing evaluation frameworks for text generation are not adequate to assess the quality of the generated outputs. |
| Approach: | They propose a framework that utilizes emergent abilities of generative pre-trained models to evaluate generated texts. |
| Outcome: | The proposed evaluation framework can achieve what one desires to evaluate for texts simply by natural language instructions. |
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| Challenge: | Existing MLLMs rely on commercial models such as GPT-4o for evaluations, but they are not universally accessible. |
| Approach: | They propose a task decomposition evaluation framework based on GPT-4o to automatically construct a specialized training dataset to break down the multifaceted evaluation process into simpler sub-tasks. |
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All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021.acl-long)
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| Challenge: | evaluators distinguish between human- and machine-authored text in three domains without training . evals' accuracy improved up to 55%, but it did not significantly improve across the three domain. |
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RecGPT: Generative Pre-training for Text-based Recommendation (2024.acl-short)
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| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
| Approach: | They present the first domain-adapted and fully-trained large language model for text-based recommendation. |
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Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation (2023.emnlp-main)
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| Challenge: | Experimental results show that GPT-k models focus more on inserting modifiers than predicting spontaneous changes in the primary subject matter. |
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INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback (2023.emnlp-main)
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| Challenge: | Existing methods to evaluate the quality of language generation do not provide explicit explanation of their verdicts. |
| Approach: | They propose a fine-grained explainable evaluation metric for text generation that harnesses human instruction and implicit knowledge of GPT-4 to fine-tune it. |
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MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance (D19-1)
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| Challenge: | Existing evaluation metrics are not capable of evaluating text quality. |
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Generative Pretraining for Paraphrase Evaluation (2022.acl-long)
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| Challenge: | ParaBLEU is a paraphrase representation learning model and evaluation metric for text generation. |
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FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023.emnlp-main)
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Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi
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Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
| Approach: | They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction. |
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SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes (2023.acl-long)
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| Challenge: | Existing learned metrics perform unsatisfactory across text generation tasks or require human annotations for training on specific tasks. |
| Approach: | They propose a self-supervised approach to train a model-based metric for text generation evaluation using sentences retrieved from a corpus. |
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