A Language Model based Framework for New Concept Placement in Ontologies

• URL: http://arxiv.org/abs/2402.17897v2
• Date: Mon, 4 Mar 2024 14:06:42 GMT
• Title: A Language Model based Framework for New Concept Placement in Ontologies
• Authors: Hang Dong, Jiaoyan Chen, Yuan He, Yongsheng Gao, Ian Horrocks
• Abstract summary: We investigate the task of inserting new concepts extracted from texts into an ontology using language models. We propose to leverage neural methods, where we apply embedding-based methods and contrastive learning. We evaluate the methods on recent datasets created using the SNOMED CT and the MedMentions entity linking benchmark.
• Score: 33.31096456574208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the task of inserting new concepts extracted from texts into an ontology using language models. We explore an approach with three steps: edge search which is to find a set of candidate locations to insert (i.e., subsumptions between concepts), edge formation and enrichment which leverages the ontological structure to produce and enhance the edge candidates, and edge selection which eventually locates the edge to be placed into. In all steps, we propose to leverage neural methods, where we apply embedding-based methods and contrastive learning with Pre-trained Language Models (PLMs) such as BERT for edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder, and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for edge selection. We evaluate the methods on recent datasets created using the SNOMED CT ontology and the MedMentions entity linking benchmark. The best settings in our framework use fine-tuned PLM for search and a multi-label Cross-encoder for selection. Zero-shot prompting of LLMs is still not adequate for the task, and we propose explainable instruction tuning of LLMs for improved performance. Our study shows the advantages of PLMs and highlights the encouraging performance of LLMs that motivates future studies.

Related papers

• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models [33.02146794292383]
  We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM) RSLLM uses a unique prompting method that combines ID-based item embeddings from conventional recommendation models with textual item features. It treats users' sequential behaviors as a distinct language and aligns the ID embeddings with the LLM's input space using a projector.
  arXiv  Detail & Related papers  (2024-12-22T09:08:46Z)
• Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback [50.84142264245052]
  This work introduces the Align-SLM framework to enhance the semantic understanding of textless Spoken Language Models (SLMs) Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO) We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation.
  arXiv  Detail & Related papers  (2024-11-04T06:07:53Z)
• Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets [19.021200954913482]
  The analysis delves into 30 existing cloze-style and multiple-choice MRC benchmark datasets. The paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods.
  arXiv  Detail & Related papers  (2024-08-04T18:57:21Z)
• CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation [18.986613405565514]
  Large Language Models (LLMs) are pretrained on vast corpora of text for sequential recommendation. We propose a two-stage LLM finetuning framework that finetunes a pretrained LLM in a two-tower fashion using a mixture of two contrastive losses and a language modeling loss. Our model significantly outperforms many state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-03T18:51:19Z)
• LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning [8.379286663107845]
  Reasoning segmentation is a novel task that enables segmentation system to reason and interpret implicit user intention. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling.
  arXiv  Detail & Related papers  (2024-04-12T18:45:51Z)
• Meta-Task Prompting Elicits Embeddings from Large Language Models [54.757445048329735]
  We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation. We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
  arXiv  Detail & Related papers  (2024-02-28T16:35:52Z)
• Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs [4.487720716313697]
  Large language models (LLMs) have revolutionized the field of natural language processing (NLP) This work explores how to leverage the information retrieval and text generation capabilities of LLMs to refine/enhance the topological structure of text-attributed graphs (TAGs) under the node classification setting.
  arXiv  Detail & Related papers  (2023-11-24T07:53:48Z)
• Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning [57.74233319453229]
  Large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to the fundamental sentence representation learning task. We propose MultiCSR, a multi-level contrastive sentence representation learning framework that decomposes the process of prompting LLMs to generate a corpus. Our experiments reveal that MultiCSR enables a less advanced LLM to surpass the performance of ChatGPT, while applying it to ChatGPT achieves better state-of-the-art results.
  arXiv  Detail & Related papers  (2023-10-17T03:21:43Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)
• Efficient Nearest Neighbor Language Models [114.40866461741795]
  Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore. We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
  arXiv  Detail & Related papers  (2021-09-09T12:32:28Z)

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.