Extracting Multi-valued Relations from Language Models

• URL: http://arxiv.org/abs/2307.03122v2
• Date: Fri, 7 Jul 2023 07:25:36 GMT
• Title: Extracting Multi-valued Relations from Language Models
• Authors: Sneha Singhania, Simon Razniewski, Gerhard Weikum
• Abstract summary: We analyze latent language representations for their potential to yield materialized multi-object relational knowledge. For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge. Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score.
• Score: 36.944060044138304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct. To overcome this limitation, we analyze these representations for their potential to yield materialized multi-object relational knowledge. We formulate the problem as a rank-then-select task. For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge. Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score. Our results highlight the difficulty of employing LMs for the multi-valued slot-filling task and pave the way for further research on extracting relational knowledge from latent language representations.

Related papers

• FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension [10.482908189805872]
  Referring Expression (REC) is a crucial cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. We have established a new REC dataset characterized by two key features. It includes negative text and images created through fine-grained editing and generation based on existing data.
  arXiv  Detail & Related papers  (2024-09-23T06:56:51Z)
• Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models [22.859955360764275]
  We introduce the MultiLingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information. We evaluate four state-of-the-art large language models on MLNeedle.
  arXiv  Detail & Related papers  (2024-08-19T17:02:06Z)
• Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
  We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance. This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation. Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
  arXiv  Detail & Related papers  (2024-07-07T17:05:24Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• LISA: Reasoning Segmentation via Large Language Model [68.24075852136761]
  We propose a new segmentation task -- reasoning segmentation. The task is designed to output a segmentation mask given a complex and implicit query text. We present LISA: large Language Instructed Assistant, which inherits the language generation capabilities of multimodal Large Language Models.
  arXiv  Detail & Related papers  (2023-08-01T17:50:17Z)
• Multilingual Entity and Relation Extraction from Unified to Language-specific Training [29.778332361215636]
  Existing approaches for entity and relation extraction tasks mainly focus on the English corpora and ignore other languages. We propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference. Our method outperforms both the monolingual and multilingual baseline methods.
  arXiv  Detail & Related papers  (2023-01-11T12:26:53Z)
• Efficient Entity Candidate Generation for Low-Resource Languages [13.789451365205665]
  Candidate generation is a crucial module in entity linking. It plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. This paper constitutes an in-depth analysis of the candidate generation problem in the context of cross-lingual entity linking.
  arXiv  Detail & Related papers  (2022-06-30T09:49:53Z)
• Contrastive Learning with Hard Negative Entities for Entity Set Expansion [29.155036098444008]
  Various NLP and IR applications will benefit from ESE due to its ability to discover knowledge. We devise an entity-level masked language model with contrastive learning to refine the representation of entities. In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities.
  arXiv  Detail & Related papers  (2022-04-16T12:26:42Z)
• ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning [97.10875695679499]
  We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text. Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
  arXiv  Detail & Related papers  (2020-12-30T03:35:22Z)

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.