GenAI4ELab/papercli-papers huggingface dataset:
Analysis of LLM’s “Spurious” Correct Answers Using Evidence Information of Multi-hop QA Datasets
Authors: Ai Ishii; Naoya Inoue; Hisami Suzuki; Satoshi Sekine
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: Recent LLMs show an impressive accuracy on one of the hallmark tasks of language understanding, namely Question Answering (QA). However, it is not clear if the correct answers provided by LLMs are actually grounded on the correct knowledge related to the question. In this paper, we use multi-hop QA datasets to evaluate the accuracy of the knowledge LLMs use to answer questions, and show that as much as 31% of the correct answers by the LLMs are in fact spurious, i.e., the knowledge LLMs used to ground the answer is wrong while the answer is correct. We present an analysis of these spurious correct answers by GPT-4 using three datasets in two languages, while suggesting future pathways to correct the grounding information using existing external knowledge bases.
Application of Generative AI as an Enterprise Wikibase Knowledge Graph Q&A System
Authors: Renê Mendes; Dimas Oliveira; Victor Garcia
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: Generative AI and Large Language Models are increasingly used in business contexts. One application involves natural language conversations contextualized by company data, which can be accomplished by Enterprise Knowledge Graphs, standardized representations of data. This paper outlines an architecture for implementation of an Enterprise Knowledge Graph using open-source Wikibase software. Additionally, it is presented a Knowledge Graph Q&A System powered by Generative AI.
KGAST: From Knowledge Graphs to Annotated Synthetic Texts
Authors: Nakanyseth Vuth; Gilles Sérasset; Didier Schwab
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets—a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST’s potential as a tool for generating synthetic data for Information Extraction tasks.
HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation
Authors: Azmine Toushik Wasi
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
Authors: Daniil Gurgurov; Mareike Hartmann; Simon Ostermann
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
Ontology-guided Knowledge Graph Construction from Maintenance Short Texts
Authors: Zeno van Cauter; Nikolay Yakovets
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education
Authors: Miquel Canal-Esteve; Yoan Gutiérrez
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
STAGE: Simplified Text-Attributed Graph Embeddings using Pre-trained LLMs
Authors: Aaron Zolnai-Lucas; Jack Boylan; Chris Hokamp; Parsa Ghaffari
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs
Authors: Moy Yuan; Andreas Vlachos
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
Fine-tuning Language Models for Triple Extraction with Data Augmentation
Authors: Yujia Zhang; Tyler Sadler; Mohammad Reza Taesiri; Wenjie Xu; Marek Reformat
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
Improving LLM-based KGQA for multi-hop Question Answering with implicit reasoning in few-shot examples
Authors: Mili Shah; Joyce Cahoon; Mirco Milletari; Jing Tian; Fotis Psallidas; Andreas Mueller; Nick Litombe
Venue: ACL 2024 (Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024))

Abstract: 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.
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Authors: Andrei C. Coman; Christos Theodoropoulos; Marie-Francine Moens; James Henderson
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leverage the intrinsic graph processing capabilities of the Transformer model and propose replacing hand-coded pooling methods with new tokens in the input, which are designed to aggregate information via explicit graph relations in the computation of attention weights. We introduce a joint text-graph Transformer model and a graph-assisted declarative pooling (GADePo) specification of the input, which provides explicit and high-level instructions for information aggregation. GADePo allows the pooling process to be guided by domain-specific knowledge or desired outcomes but still learned by the Transformer, leading to more flexible and customisable pooling strategies. We evaluate our method across diverse datasets and models and show that our approach yields promising results that are consistently better than those achieved by the hand-coded pooling functions.
KaPQA: Knowledge-Augmented Product Question-Answering
Authors: Swetha Eppalapally; Daksh Dangi; Chaithra Bhat; Ankita Gupta; Ruiyi Zhang; Shubham Agarwal; Karishma Bagga; Seunghyun Yoon; Nedim Lipka; Ryan Rossi; Franck Dernoncourt
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.
Collecting High-quality Multi-modal Conversational Search Data for E-Commerce
Authors: Marcus Collins; Oleg Rokhlenko; Eugene Agichtein; Shervin Malmasi
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated large language model chatbots, dialogue managers, response rankers, and recommenders. The problem is exacerbated for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent must interact intelligently with a customer using both textual and visual information and incorporate results from external search systems, such as a product catalog. Yet, it remains an open question how to best crowd-source large-scale, naturalistic multi-modal dialogue and action data, required to train such an artificial agent. We describe our crowd-sourced task where one worker (the Buyer) plays the role of the customer, and another (the Seller) plays the role of the sales agent. We identify subtle interactions between one worker’s environment and their partner’s behavior mediated by workers’ word choice. We find that limiting information presented to the Buyer, both in their backstory and by the Seller, improves conversation quality. We also show how conversations are improved through minimal automated Seller “coaching”. While typed and spoken messages are slightly different, the differences are not as large as frequently assumed. We plan to release our platform code and the resulting dialogues to advance research on conversational search agents.
Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation
Authors: Yuxin Liang; Zhuoyang Song; Hao Wang; Jiaxing Zhang
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85% accuracy in knowledge state probing. However, LLMs often fail to faithfully express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, DreamCatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.
Aggregating Impressions on Celebrities and their Reasons from Microblog Posts and Web Search Pages
Authors: Hibiki Yokoyama; Rikuto Tsuchida; Kosei Buma; Sho Miyakawa; Takehito Utsuro; Masaharu Yoshioka
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: This paper aims to augment fans’ ability to critique and exploreinformation related to celebrities of interest. First, we collect postsfrom X (formerly Twitter) that discuss matters related to specificcelebrities. For the collection of major impressions from these posts,we employ ChatGPT as a large language model (LLM) to analyze andsummarize key sentiments. Next, based on collected impressions, wesearch for Web pages and collect the content of the top 30 ranked pagesas the source for exploring the reasons behind those impressions. Oncethe Web page content collection is complete, we collect and aggregatedetailed reasons for the impressions on the celebrities from the contentof each page. For this part, we continue to use ChatGPT, enhanced bythe retrieval augmented generation (RAG) framework, to ensure thereliability of the collected results compared to relying solely on theprior knowledge of the LLM. Evaluation results by comparing a referencethat is manually collected and aggregated reasons with those predictedby ChatGPT revealed that ChatGPT achieves high accuracy in reasoncollection and aggregation. Furthermore, we compared the performance ofChatGPT with an existing model of mT5 in reason collection and confirmedthat ChatGPT exhibits superior performance.
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation
Authors: Taeho Hwang; Soyeong Jeong; Sukmin Cho; SeungYoon Han; Jong Park
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks.However, LLMs still struggle with generating non-factual responses due to limitations in their parametric memory.Retrieval-Augmented Generation (RAG) systems address this issue by incorporating external knowledge with a retrieval module.Despite their successes, however, current RAG systems face challenges with retrieval failures and the limited ability of LLMs to filter out irrelevant information.Therefore, in this work, we propose DSLR (Document Refinement with Sentence-Level Re-ranking and Reconstruction), an unsupervised framework that decomposes retrieved documents into sentences, filters out irrelevant sentences, and reconstructs them again into coherent passages.We experimentally validate DSLR on multiple open-domain QA datasets and the results demonstrate that DSLR significantly enhances the RAG performance over conventional fixed-size passage.Furthermore, our DSLR enhances performance in specific, yet realistic scenarios without the need for additional training, providing an effective and efficient solution for refining retrieved documents in RAG systems.
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
Authors: SeongIl Park; Seungwoo Choi; Nahyun Kim; Jay-Yoon Lee
Venue: ACL 2024 (Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP)

Abstract: Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as cases, to boost the model’s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.