KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation

• URL: http://arxiv.org/abs/2005.11768v2
• Date: Fri, 24 Jul 2020 06:39:14 GMT
• Title: KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation
• Authors: Jiajing Wan and Xinting Huang
• Abstract summary: We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task.
• Score: 4.94950858749529
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.

Related papers

• SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection [51.99159169107426]
  We present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions with reference standards. We introduce three distinct methods that exhibit strong performance metrics.
  arXiv  Detail & Related papers  (2024-04-09T09:03:44Z)
• InternLM2 Technical Report [159.70692271378581]
  This paper introduces InternLM2, an open-source Large Language Models (LLMs) that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2024-03-26T00:53:24Z)
• Limits of Transformer Language Models on Learning to Compose Algorithms [77.2443883991608]
  We evaluate training LLaMA models and prompting GPT-4 and Gemini on four tasks demanding to learn a composition of several sub-tasks. Our results indicate that compositional learning in state-of-the-art Transformer language models is highly sample inefficient.
  arXiv  Detail & Related papers  (2024-02-08T16:23:29Z)
• NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models [4.329463429688995]
  This paper describes the NOWJ1 Team's approach for the Automated Legal Question Answering Competition (ALQAC) 2023. For the document retrieval task, we implement a pre-processing step to overcome input limitations and apply learning-to-rank methods to consolidate features from various models. We incorporate state-of-the-art models to develop distinct systems for each sub-task, utilizing both classic statistical models and pre-trained Language Models.
  arXiv  Detail & Related papers  (2023-09-16T18:32:15Z)
• Zero-shot Visual Question Answering with Language Model Feedback [83.65140324876536]
  We propose a language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA) Our approach employs the generated captions by a captioning model as the context of an answer prediction model, which is a Pre-trained Language model (PLM)
  arXiv  Detail & Related papers  (2023-05-26T15:04:20Z)
• Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification [58.720142291102135]
  This case study investigates the task of job classification in a real-world setting. The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
  arXiv  Detail & Related papers  (2023-03-13T14:09:53Z)
• ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search [4.220439000486713]
  We propose a robust multilingual model to improve the quality of search results. In pre-training stage, we adopt mlm task, classification task and contrastive learning task. In fine-tuning stage, we use confident learning, exponential moving average method (EMA), adversarial training (FGM) and regularized dropout strategy (R-Drop)
  arXiv  Detail & Related papers  (2023-01-31T07:31:34Z)
• Unifying Language Learning Paradigms [96.35981503087567]
  We present a unified framework for pre-training models that are universally effective across datasets and setups. We show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
  arXiv  Detail & Related papers  (2022-05-10T19:32:20Z)
• ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning [16.151203366447962]
  We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively.
  arXiv  Detail & Related papers  (2021-02-25T13:03:05Z)
• IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation [8.971288666318719]
  This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading of Abstract Meaning. We well-design many simple and effective approaches adapted to the backbone model (RoBERTa) Experimental results show that our approaches achieve significant performance compared with the baseline systems.
  arXiv  Detail & Related papers  (2021-02-25T10:51:48Z)
• QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model [2.728575246952532]
  In this paper, we present language model system submitted to SemEval-2020 Task 4 competition: "Commonsense Validation and Explanation" We implemented with transfer learning using pretrained language models (BERT, XLNet, RoBERTa, and ALBERT) and fine-tune them on this task. The ensembled model better solves this problem, making the model's accuracy reached 95.9% on subtask A, which just worse than human's by only 3% accuracy.
  arXiv  Detail & Related papers  (2020-09-06T05:12:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.