Papers by Barlas Oguz
Multi-Task Retrieval for Knowledge-Intensive Tasks (2021.acl-long)
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Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, Gargi Ghosh
| Challenge: | Knowledge-intensive tasks require large amounts of knowledge about the world . recent neural retrieval models achieve better results by learning directly from task-specific training data. |
| Approach: | They propose a multi-task trained neural retrieval model that can be universally trained on a wide variety of problems. |
| Outcome: | The proposed model outperforms specialised retrievers on a few-shot setting and matches or improves state-of-the-art on multiple benchmarks. |
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2022.findings-naacl)
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| Challenge: | Existing approaches to answer open domain questions rely on unlabeled text or synthetically generated question-answer pairs. |
| Approach: | They propose a large-scale open-domain question-answering dataset based on the Common Crawl project that can be used to in-domain pre-train popular language models. |
| Outcome: | The proposed dataset achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks. |
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)
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Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining. |
| Approach: | They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead. |
| Outcome: | The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes. |
Joint Verification and Reranking for Open Fact Checking Over Tables (2021.acl-long)
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Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, Sebastian Riedel
| Challenge: | Existing research into structured data has focused on textual data and the closed-domain setting is not reflective of real-world fact checking tasks. |
| Approach: | They propose a joint reranking-and-verification model which fuses evidence documents in the verification component and a heuristic retrieval baseline. |
| Outcome: | The proposed model achieves comparable performance to the closed-domain state-of-the-art on the TabFact dataset and significantly improves over a heuristic retrieval baseline. |
Binary and Ternary Natural Language Generation (2023.acl-long)
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| Challenge: | ternary and binary neural networks have proven difficult to optimize since both parameter and output space are discretized . authors demonstrate ternaries and binary models on downstream tasks of summarization and machine translation . |
| Approach: | They propose to use ternary and binary neural networks to optimize for multiplication-free computation . they propose to apply statistics-based quantization for the weights and elastic quantization of the activations to the transformer text generation model. |
| Outcome: | The proposed model outperforms the best existing models on machine translation tasks. |
MLQA: Evaluating Cross-lingual Extractive Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. |
| Approach: | They present a multi-way aligned extractive QA evaluation benchmark in 7 languages . they evaluate state-of-the-art cross-lingual models and machine-translation-based baselines . |
| Outcome: | The proposed model is based on MLQA, which has over 12K instances in english and 5K in each other language. |
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval (2023.acl-long)
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Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing multi-vector retrieval methods are slower and require more space to store indices compared to their single-vektor counterparts. |
| Approach: | They propose a multi-vector retrieval method that uses dynamic lexical routing to route different token vectors to the predicted lexicals. |
| Outcome: | The proposed method achieves state-of-the-art performance on several benchmark datasets while being nearly 40 times faster than the current state-out-of the-art method. |
Bridging the Training-Inference Gap for Dense Phrase Retrieval (2022.findings-emnlp)
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| Challenge: | Existing methods for building dense retrievers are often misaligned and do not reflect retrieval scenario at inference time. |
| Approach: | They propose a way to validate dense retrievers using a small subset of the entire corpus. |
| Outcome: | The proposed model improves top-1 phrase retrieval accuracy by 2 3 points and top-20 passage retrieval by 2 4 points for open-domain question answering. |
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One? (2022.findings-emnlp)
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Xilun Chen, Kushal Lakhotia, Barlas Oguz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih
| Challenge: | Existing sparse retrievers lack the ability to match salient phrases and rare entities in the query. |
| Approach: | They introduce a dense Lexical Model that can be trained to imitate a sparse one. |
| Outcome: | The proposed model outperforms sparse retrievers on a range of tasks including five question answering datasets and the MS MARCO passage retrieval. |
Boosted Dense Retriever (2022.naacl-main)
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| Challenge: | DrBoost is a dense retrieval ensemble that is trained in stages to correct retrieval mistakes . it produces representations which are 4x more compact, while delivering comparable retrieval results. |
| Approach: | They propose a dense retrieval ensemble inspired by boosting that is trained in stages . they produce representations which are 4x more compact, while delivering comparable retrieval results . |
| Outcome: | The proposed model performs surprisingly well under approximate search with coarse quantization, reducing latency and bandwidth needs by another 4x. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2024.findings-acl)
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Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Several post-training quantization methods have been shown to perform well down to 8-bits. |
| Approach: | They propose a data-free distillation method that leverages generations produced by the pre-trained model to quantize any generative model independent of its training data. |
| Outcome: | The proposed method outperforms SoTA PTQ and LLaMA models at low bit precision. |
A Study on the Efficiency and Generalization of Light Hybrid Retrievers (2023.acl-short)
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Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
| Challenge: | Recent research focuses on building neural retrievers which learn dense embeddings of query and document into a semantic space. |
| Approach: | They propose to use an indexing-efficient dense retriever to reduce hybrid retrievers' memory by using the state-based indexing algorithm. |
| Outcome: | The proposed hybrid retriever saves 13 memory while maintaining 98.0% performance on out-of-domain datasets and adversarial attacks datasets. |
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)
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Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
| Challenge: | a recent study aims to answer factual questions using a structured knowledge base (KBQA). |
| Approach: | They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge. |
| Outcome: | The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods. |
Effective Long-Context Scaling of Foundation Models (2024.naacl-long)
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Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, Hao Ma
| Challenge: | Large language models (LLMs) are rapidly deployed and continue to evolve through scaling. |
| Approach: | They propose a method to train strong long-context LLMs that are capable of utilizing massive context windows of up to 32,000 tokens. |
| Outcome: | The proposed model can surpass gpt-3.5-turbo-16k's overall performance on long-context benchmarks with a cost-effective instruction tuning procedure that is free of expensive annotations. |
Simple Local Attentions Remain Competitive for Long-Context Tasks (2022.naacl-main)
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Wenhan Xiong, Barlas Oguz, Anchit Gupta, Xilun Chen, Diana Liskovich, Omer Levy, Scott Yih, Yashar Mehdad
| Challenge: | Existing models for NLP tasks require long text sequences beyond the length limit of pretrained models. |
| Approach: | They propose to pretrain large-size NLP models using the same long-doc corpus and fine tune them for real-world long-context tasks. |
| Outcome: | The proposed models can perform better under standard pretraining paradigms than longformer and Longformer. |
Domain-matched Pre-training Tasks for Dense Retrieval (2022.findings-naacl)
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Barlas Oguz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Scott Yih, Sonal Gupta, Yashar Mehdad
| Challenge: | Existing approaches to improve performance of pre-training tasks are needed. |
| Approach: | They propose to pre-train large bi-encoder models on a recently released set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting reddit conversation dataset. |
| Outcome: | The proposed model can be pre-trained on a set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting dataset of Reddit conversations. |
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown strong effectiveness and robustness when fine-tuned as dense retrievers. |
| Approach: | They propose a training framework that leverages pruned LLMs to train smaller generalizable dense retrievers. |
| Outcome: | The proposed training framework offers better multilingual and long-context capabilities than traditional encoder-based retrievers and achieves strong performance across multiple tasks and languages. |
Text-guided 3D Human Generation from 2D Collections (2023.findings-emnlp)
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| Challenge: | 3D human modeling is used for engaging interaction in gaming, film, and animation. however, the customization of characters is crucial for creativity and scalability. |
| Approach: | They propose a 3D human generation using fashion descriptions to enhance 3D geometry transformation and fine-grained consistency. |
| Outcome: | The proposed model can generate a 3D human, guided by a fashion description, with high efficiency. |
How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval (2023.findings-emnlp)
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Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing techniques to improve dense retrieval suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, some argue due to the limited model capacity. |
| Approach: | They propose to use diverse queries and sources of supervision to train a generalizable DR to achieve high accuracy in both supervised and zero-shot retrieval. |
| Outcome: | The proposed DR can achieve state-of-the-art in supervised and zero-shot evaluations without increasing model size. |