Papers by Rami Aly
Learning to Generate Answers with Citations via Factual Consistency Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. |
| Approach: | They propose a weakly-supervised fine-tuning method leveraging factual consistency models (FCMs) they propose to generate citations to retrieved text passages alongside LLM-produced statements . |
| Outcome: | The proposed method improves on the ALCE few-shot citation benchmark with various instruction-tuned LLMs with an average improvement of 34.1, 15.5, and 10.5 citation F1 points. |
Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification (2022.emnlp-main)
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| Challenge: | Recent evidence retrieval approaches rely on heuristics and assume hyperlinks between documents. |
| Approach: | They propose a retrieval method that combines a retriever and a proof system that reranks documents and reorders them . |
| Outcome: | The proposed method exceeds or is on par with the current state-of-the-art on FEVER, HoVer and FEVEROUS-S while using 5 to 10 times less memory than competing systems. |
Zero-Shot Fact Verification via Natural Logic and Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent advancements in fact verification systems with natural logic have enhanced their explainability by aligning claims with evidence through set-theoretic operators, providing faithful justifications. |
| Approach: | They propose a method that utilizes the generalization capabilities of instruction-tuned large language models to provide faithful justifications. |
| Outcome: | The proposed method outperforms other systems that were not specifically trained on natural logic data, and achieves an average accuracy improvement of 8.96 points over the baseline. |
QA-NatVer: Question Answering for Natural Logic-based Fact Verification (2023.emnlp-main)
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| Challenge: | Recent work has focused on natural logic, which operates directly on natural language by capturing the semantic relation of spans between an aligned claim and its evidence via set-theoretic operators. |
| Approach: | They propose to use question answering to predict natural logic operators using generalization capabilities of instruction-tuned language models. |
| Outcome: | The proposed approach outperforms the best baseline on a Danish verification dataset by 4.3 accuracy points. |
Hierarchical Multi-label Classification of Text with Capsule Networks (P19-2)
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| Challenge: | In hierarchical multi-label classification, samples are classified into one or multiple class labels organized in a structured label hierarchy. |
| Approach: | They apply and compare shallow capsule networks for hierarchical multi-label text classification and introduce a new real-world scenario dataset. |
| Outcome: | The proposed model outperforms neural networks and non-neural network architectures on a real-world scenario dataset. |
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification (2021.acl-long)
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| Challenge: | Named entity recognition and classification (NERC) tasks require annotated data for the target classes during training. |
| Approach: | They propose a novel approach that leverages the fact that textual descriptions for many entity classes occur naturally. |
| Outcome: | The proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. |
Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings (P19-1)
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| Challenge: | Existing approaches to domain-specific taxonomy induction from text are relying on distributional semantics for hyponym-hypernym relationships, but many of them learn prototypical hypernymes, not taking into account the relation between both terms in classification. |
| Approach: | They propose to use Poincaré embeddings to improve existing approaches to domain-specific taxonomy induction from text as a signal for relocating wrong hyponym terms and attaching disconnected terms in a taxonomies. |
| Outcome: | The proposed method significantly improves state-of-the-art methods on the SemEval-2016 Task 13 on taxonomy extraction. |
Automated Few-Shot Classification with Instruction-Finetuned Language Models (2023.findings-emnlp)
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| Challenge: | Existing few-shot learning approaches combine language models with prompts, but they often require domain knowledge and substantial guesswork. |
| Approach: | They propose a method to eliminate the need for handcrafted prompts by generating two distinct, semantically meaningful class descriptions and a selection mechanism via cross-validation. |
| Outcome: | The proposed method outperforms state-of-the-art few-shot learning methods over 12 datasets, spanning 8 classification tasks. |