Thailand

Knowledge Graphs (KGs) serving as semantic networks, prove highly effective in managing complex interconnected data in different domains, by offering a unified, contextualized, and structured representation with flexibility that allows for easy adaptation to evolving knowledge. Processing complex Human Resources (HR) data, KGs can help in different HR functions like recruitment, job matching, identifying learning gaps, and enhancing employee retention. Despite their potential, limited efforts have been made to implement practical HR knowledge graphs. This study addresses this gap by presenting a framework for effectively developing HR knowledge graphs from documents using Large Language Models. The resulting KG can be used for a variety of downstream tasks, including job matching, identifying employee skill gaps, and many more. In this work, we showcase instances where HR KGs prove instrumental in precise job matching, yielding advantages for both employers and employees. Empirical evidence from experiments with information propagation in KGs and Graph Neural Nets, along with case studies underscores the effectiveness of KGs in tasks such as job and employee recommendations and job area classification. Code and data are available at : https://github.com/azminewasi/HRGraph

ACL 2024

HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation

graph-based recommendations

information propagation in knowledge graphs

human resource

job recommendation

graph neural networks

large language models

knowledge graphs

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop paper

This paper explores the integration of graph knowledge from linguistic ontologies into multilingual Large Language Models (LLMs) using adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER). Building upon successful parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, we propose a similar approach for incorporating knowledge from multilingual graphs, connecting concepts in various languages with each other through linguistic relationships, into multilingual LLMs for LRLs. Specifically, we focus on eight LRLs — Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala — and employ language-specific adapters fine-tuned on data extracted from the language-specific section of ConceptNet, aiming to enable knowledge transfer across the languages covered by the knowledge graph. We compare various fine-tuning objectives, including standard Masked Language Modeling (MLM), MLM with full-word masking, and MLM with targeted masking, to analyze their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, we assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER, providing insights into the potential benefits of adapting language models for low-resource scenarios.

Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

Large-scale knowledge graph construction remains infeasible since it requires significant human-expert involvement. Further complications arise when building graphs from domain-specific data due to their unique vocabularies and associated contexts. In this work, we demonstrate the ability of open-source large language models (LLMs), such as Llama-2 and Llama-3, to extract facts from domain-specific Maintenance Short Texts (MSTs). We employ an approach which combines ontology-guided triplet extraction and in-context learning. By using only 20 semantically similar examples with the Llama-3-70B-Instruct model, we achieve performance comparable to previous methods that relied on fine-tuning techniques like SpERT and REBEL. This indicates that domain-specific fact extraction can be accomplished through inference alone, requiring minimal labeled data. This opens up possibilities for effective and efficient semi-automated knowledge graph construction for domain-specific data.

Ontology-guided Knowledge Graph Construction from Maintenance Short Texts

This article argues that digital educational content should be structured as knowledge graphs (KGs). Unlike traditional repositories such as Moodle, a KG offers a more flexible representation of the relationships between concepts, facilitating intuitive navigation and discovery of connections. In addition, it integrates effectively with Large Language Models, enhancing personalized explanations, answers, and recommendations. This article studies different proposals based on semantics and knowledge modelling to determine the most appropriate ways to strengthen intelligent educational technologies.

Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education

We present STAGE, a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. STAGE achieves competitive results on various node classification benchmarks while also maintaining a simplicity in implementation relative to current state-of-the-art (SoTA) techniques. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training, enabling pipelines that are simpler than current SoTA approaches which require multiple expensive training and prompting stages. We also implement diffusion-pattern GNNs in an effort to make this pipeline scalable to graphs beyond academic benchmarks.

STAGE: Simplified Text-Attributed Graph Embeddings using Pre-trained LLMs

Despite progress in automated fact-checking, most systems require a significant amount of labeled training data, which is expensive. In this paper, we propose a novel zero-shot method, which instead of operating directly on the claim and evidence sentences, decomposes them into semantic triples augmented using external knowledge graphs, and uses large language models trained for natural language inference. This allows it to generalize to adversarial datasets and domains that supervised models require specific training data for. Our empirical results show that our approach outperforms previous zero-shot approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being comparable or better than supervised models on the adversarial and the out-of-domain datasets.

Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs

Advanced language models with impressive capabilities to process textual information can more effectively extract high-quality triples, which are the building blocks of knowledge graphs. 
Our work examines language models' abilities to extract entities and the relationships between them. We use a diverse data augmentation process to fine-tune large language models to extract triples from the text. Fine-tuning is performed using a mix of trainers from HuggingFace and five public datasets, such as different variations of the WebNLG, SKE, DocRed, FewRel, and KELM. Evaluation involves comparing model outputs with test-set triples based on several criteria, such as type, partial, exact, and strict accuracy.
The obtained results outperform ChatGPT and even match or exceed the performance of GPT-4.

Fine-tuning Language Models for Triple Extraction with Data Augmentation

Large language models (LLMs) have shown remarkable capabilities in generating natural language texts for various tasks. However, using LLMs for question answering on knowledge graphs still remains a challenge, especially for questions requiring multi-hop reasoning. In this paper, we present a novel planned query guidance approach that improves large language model (LLM) performance in multi-hop question answering on knowledge graphs (KGQA). We do this by designing few-shot examples that implicitly demonstrate a systematic reasoning methodology to answer multi-hop questions. We evaluate our approach for two graph query languages, Cypher and SPARQL, and show that the queries generated using our strategy outperform the queries generated using a baseline LLM and typical few-shot examples by up to 24.66\% and 7.7\% in execution match accuracy for the MetaQA and the Spider benchmarks respectively. We also conduct an ablation study to analyze the incremental effects of the different techniques of designing few-shot examples. Our results suggest that our approach enables the LLM to effectively leverage the few-shot examples to generate queries for multi-hop KGQA.

Improving LLM-based KGQA for multi-hop Question Answering with implicit reasoning in few-shot examples

The evolving landscape of Large Language Models (LLMs) and conversational assistants has ushered in a need for dynamic, up-to-date, scalable, and configurable conversational datasets to train and evaluate systems.
Ideally, these datasets are tailored for different user interaction settings, such as text and voice, all of which introduce distinct nuances and modeling challenges.
Knowledge Graphs (KGs), with their structured and continuously evolving nature, serve as an ideal reservoir for harnessing current and precise knowledge.
While there exist human-curated conversational datasets grounded on KGs, it is hard to rely solely on them, as the information needs of users are in constant flux.
Addressing this lacuna, we introduce ConvKGYarn, a scalable and effective method to generate up-to-date, configurable synthetic conversational KGQA datasets.
Qualitative psychometric analyses elucidate the effectiveness of ConvKGYarn in generating high-quality conversational data that rivals a popular conversational KGQA dataset on various metrics while making strides in additional desirable properties like adhering to human interaction configurations and functioning at a much larger scale.
We further demonstrate the utility of ConvKGYarn by testing LLMs on varied conversations to explore model behavior on conversational KGQA sets with different configurations grounded on the same fact set from the KG.
Through our work, we aim to fortify the underpinnings of KGQA and evaluate the parametric knowledge of LLMs.

ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models

This paper presents our submission to the L+M-24 shared task, focused on translating molecular structures into natural language descriptions, known as the molecule captioning task. We selected a small language model (SLM), Phi-3-mini-4k, to evaluate the impact of continued pretraining and instruction tuning for domain-specific chemical knowledge. The Phi-3 model was continued pretrained with 90M chemistry textbooks and abstracts, followed by instruction tuning on 150K question answering sets of SMILES and general chemistry knowledge. Despite the continued pretraining phase not including direct exposure to SMILES representations, it significantly enhanced the Phi-3 model's performance, a 300% increase for the BLEU scores, in the molecule captioning task. The code and model are released at \url{https://github.com/bluesky333/Phi3KnowChem} to facilitate research in chemical small language modeling.

Knowlab's Submission to L+M Shared Task: All you need is continued pretraining of \chemistry texts even for molecule captioning

LLMs have revolutionized the landscape of information retrieval and knowledge dissemination. However, their application in specialized areas is often hindered by limitations such as factual inaccuracies and hallucinations, especially in long-tail knowledge distributions. In this work, we explore the potential of retrieval-augmented generation (RAG) models in performing long-form question answering (LFQA) on a specially curated niche and custom knowledge domain. We present VedantaNY-10M, a dataset curated from extensive public discourses on the ancient Indian philosophy of Advaita Vedanta. We develop and benchmark a RAG model against a standard, non-RAG LLM, focusing on transcription, retrieval, and generation performance. A human evaluation involving computational linguists and domain experts, shows that the RAG model significantly outperforms the standard model in producing factual, comprehensive responses having fewer hallucinations. In addition, we find that a keyword-based hybrid retriever that focuses on unique low-frequency words further improves results. Our study provides insights into the future development of real-world RAG models for custom and niche areas of knowledge.

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