Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval

• URL: http://arxiv.org/abs/2412.13205v1
• Date: Tue, 03 Dec 2024 10:52:49 GMT
• Title: Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval
• Authors: Quang Hoang Trung, Nguyen Van Hoang Phuc, Le Trung Hoang, Quang Huu Hieu, Vo Nguyen Le Duy,
• Abstract summary: We introduce a new dataset specifically designed for Japanese legal contexts. In the first phase, the model learns a broad understanding of global contexts, enhancing its generalization. In the second phase, the model is fine-tuned to address complex queries specific to legal scenarios. Our pipeline proves effective in English contexts, surpassing comparable baselines on the MS MARCO dataset.
• Score: 6.058427379240698
• License:
• Abstract: Text Retrieval (TR) involves finding and retrieving text-based content relevant to a user's query from a large repository, with applications in real-world scenarios such as legal document retrieval. While most existing studies focus on English, limited work addresses Japanese contexts. In this paper, we introduce a new dataset specifically designed for Japanese legal contexts and propose a novel two-phase pipeline tailored to this domain. In the first phase, the model learns a broad understanding of global contexts, enhancing its generalization and adaptability to diverse queries. In the second phase, the model is fine-tuned to address complex queries specific to legal scenarios. Extensive experiments are conducted to demonstrate the superior performance of our method, which outperforms existing baselines. Furthermore, our pipeline proves effective in English contexts, surpassing comparable baselines on the MS MARCO dataset. We have made our code publicly available on GitHub, and the model checkpoints are accessible via HuggingFace.

Related papers

• Optimizing Multi-Stage Language Models for Effective Text Retrieval [0.0]
  We introduce a novel two-phase text retrieval pipeline optimized for Japanese legal datasets. Our method leverages advanced language models to achieve state-of-the-art performance. To further enhance robustness and adaptability, we incorporate an ensemble model that integrates multiple retrieval strategies.
  arXiv  Detail & Related papers  (2024-12-26T16:05:19Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Evaluating Embedding APIs for Information Retrieval [51.24236853841468]
  We evaluate the capabilities of existing semantic embedding APIs on domain generalization and multilingual retrieval. We find that re-ranking BM25 results using the APIs is a budget-friendly approach and is most effective in English. For non-English retrieval, re-ranking still improves the results, but a hybrid model with BM25 works best, albeit at a higher cost.
  arXiv  Detail & Related papers  (2023-05-10T16:40:52Z)
• Attentive Deep Neural Networks for Legal Document Retrieval [2.4350217735794337]
  We study the use of attentive neural network-based text representation for statute law document retrieval. We develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. Experimental results show that Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages.
  arXiv  Detail & Related papers  (2022-12-13T01:37:27Z)
• XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing [70.40401197026925]
  In-context learning using large language models has recently shown surprising results for semantic parsing tasks. This work introduces the XRICL framework, which learns to retrieve relevant English exemplars for a given query. We also include global translation exemplars for a target language to facilitate the translation process for large language models.
  arXiv  Detail & Related papers  (2022-10-25T01:33:49Z)
• UnifieR: A Unified Retriever for Large-Scale Retrieval [84.61239936314597]
  Large-scale retrieval is to recall relevant documents from a huge collection given a query. Recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. We propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability.
  arXiv  Detail & Related papers  (2022-05-23T11:01:59Z)
• A Multi-Perspective Architecture for Semantic Code Search [58.73778219645548]
  We propose a novel multi-perspective cross-lingual neural framework for code--text matching. Our experiments on the CoNaLa dataset show that our proposed model yields better performance than previous approaches.
  arXiv  Detail & Related papers  (2020-05-06T04:46:11Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

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.