Search Results for author: Michael Bendersky

Found 53 papers, 6 papers with code

Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization

no code implementations5 May 2024 Hamed Zamani, Michael Bendersky

This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work.

Fact Verification Open-Domain Question Answering +4

Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience

no code implementations20 Feb 2024 Tao Chen, Siqi Zuo, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky

To this end, we introduce an LLM-based approach to generate a dataset that consists of textual explanations of why real users make certain purchase decisions.

Explanation Generation Recommendation Systems

Bridging the Preference Gap between Retrievers and LLMs

no code implementations13 Jan 2024 Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky

Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM.

Question Answering Retrieval

Creator Context for Tweet Recommendation

no code implementations29 Nov 2023 Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork, Matt Colen, Sergey Levi, Vladimir Ofitserov, Tanvir Amin

In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet.

Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning

no code implementations16 Nov 2023 Kazuma Hashimoto, Karthik Raman, Michael Bendersky

Unlike the previous work, we introduce a novel labeling method, incremental utility, which estimates how much incremental knowledge is brought into the LLMs by a demonstration.

In-Context Learning Multi-class Classification +1

Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?

no code implementations15 Nov 2023 Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky

In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers?

Instruction Following Language Modelling +2

On What Basis? Predicting Text Preference Via Structured Comparative Reasoning

no code implementations14 Nov 2023 Jing Nathan Yan, Tianqi Liu, Justin T Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Yao Zhao, Charu Lakshmanan, Yair Kurzion, Alexander M. Rush, Jialu Liu, Michael Bendersky

Comparative reasoning plays a crucial role in text preference prediction; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning.

Hallucination Retrieval

It's All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction

no code implementations14 Nov 2023 Aditi Chaudhary, Karthik Raman, Michael Bendersky

Recent developments in large language models (LLMs) have shown promise in their ability to generate synthetic query-document pairs by prompting with as few as 8 demonstrations.

Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning

no code implementations13 Nov 2023 Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky

To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.

In-Context Learning Language Modelling +2

Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

no code implementations21 Oct 2023 Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang, Michael Bendersky

We propose to incorporate fine-grained relevance labels into the prompt for LLM rankers, enabling them to better differentiate among documents with different levels of relevance to the query and thus derive a more accurate ranking.

Automated Evaluation of Personalized Text Generation using Large Language Models

no code implementations17 Oct 2023 Yaqing Wang, Jiepu Jiang, Mingyang Zhang, Cheng Li, Yi Liang, Qiaozhu Mei, Michael Bendersky

Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context.

Text Generation text similarity

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

1 code implementation8 Oct 2023 Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Gen Li, Ajay Jaiswal, Mykola Pechenizkiy, Yi Liang, Michael Bendersky, Zhangyang Wang, Shiwei Liu

Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.

Network Pruning

Ambiguity-Aware In-Context Learning with Large Language Models

no code implementations14 Sep 2023 Lingyu Gao, Aditi Chaudhary, Krishna Srinivasan, Kazuma Hashimoto, Karthik Raman, Michael Bendersky

In-context learning (ICL) i. e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required.

In-Context Learning Semantic Similarity +3

Teach LLMs to Personalize -- An Approach inspired by Writing Education

no code implementations15 Aug 2023 Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, Michael Bendersky

Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation.

Retrieval Text Generation

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

no code implementations30 Jun 2023 Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky

Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem.

Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation

no code implementations8 May 2023 Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Jialu Liu, Michael Bendersky, Marc Najork, Chao Zhang

In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution.

Knowledge Distillation

Query Expansion by Prompting Large Language Models

no code implementations5 May 2023 Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, Michael Bendersky

Query expansion is a widely used technique to improve the recall of search systems.

Multivariate Representation Learning for Information Retrieval

no code implementations27 Apr 2023 Hamed Zamani, Michael Bendersky

Instead of learning a vector for each query and document, our framework learns a multivariate distribution and uses negative multivariate KL divergence to compute the similarity between distributions.

Information Retrieval Representation Learning +1

LaMP: When Large Language Models Meet Personalization

1 code implementation22 Apr 2023 Alireza Salemi, Sheshera Mysore, Michael Bendersky, Hamed Zamani

This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models for producing personalized outputs.

Language Modelling Natural Language Understanding +4

Metric-agnostic Ranking Optimization

no code implementations17 Apr 2023 Qingyao Ai, Xuanhui Wang, Michael Bendersky

To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in \textit{Metric-agnostic Ranking Optimization}.

Information Retrieval Learning-To-Rank +2

"Why is this misleading?": Detecting News Headline Hallucinations with Explanations

no code implementations12 Feb 2023 Jiaming Shen, Jialu Liu, Dan Finnie, Negar Rahmati, Michael Bendersky, Marc Najork

With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation.

Hallucination Headline Generation +1

Towards Disentangling Relevance and Bias in Unbiased Learning to Rank

no code implementations28 Dec 2022 Yunan Zhang, Le Yan, Zhen Qin, Honglei Zhuang, Jiaming Shen, Xuanhui Wang, Michael Bendersky, Marc Najork

We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.

Learning-To-Rank

What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis

no code implementations21 Dec 2022 Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, Michael Bendersky

Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters.

Language Modelling Large Language Model +1

Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance

no code implementations2 Nov 2022 Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, Bing-Rong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork

As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design.

Learning-To-Rank regression

RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses

no code implementations12 Oct 2022 Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky

Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT.

Decoder

Retrieval-Enhanced Machine Learning

no code implementations2 May 2022 Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, Michael Bendersky

Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models.

BIG-bench Machine Learning Information Retrieval +1

Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models

no code implementations25 Jan 2022 Tao Chen, Mingyang Zhang, Jing Lu, Michael Bendersky, Marc Najork

In this work, we carefully select five datasets, including two in-domain datasets and three out-of-domain datasets with different levels of domain shift, and study the generalization of a deep model in a zero-shot setting.

Language Modelling Passage Retrieval +1

Rank4Class: A Ranking Formulation for Multiclass Classification

no code implementations17 Dec 2021 Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes.

Classification Image Classification +4

Improving Neural Ranking via Lossless Knowledge Distillation

no code implementations30 Sep 2021 Zhen Qin, Le Yan, Yi Tay, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized identically to their teachers.

Knowledge Distillation Learning-To-Rank

Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank

no code implementations29 Sep 2021 Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.

Image Classification Information Retrieval +4

Dynamic Language Models for Continuously Evolving Content

no code implementations11 Jun 2021 Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork

To this end, we both explore two different vocabulary composition methods, as well as propose three sampling methods which help in efficient incremental training for BERT-like models.

LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding

no code implementations16 Apr 2021 Te-Lin Wu, Cheng Li, Mingyang Zhang, Tao Chen, Spurthi Amba Hombaiah, Michael Bendersky

text, table, image) and propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document.

document understanding

Natural Language Understanding with Privacy-Preserving BERT

no code implementations15 Apr 2021 Chen Qu, Weize Kong, Liu Yang, Mingyang Zhang, Michael Bendersky, Marc Najork

We investigate the privacy and utility implications of applying dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU applications.

Language Modelling Natural Language Understanding +1

Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees

no code implementations ICLR 2021 Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.

Learning-To-Rank

Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach

no code implementations2 Oct 2020 Saar Kuzi, Mingyang Zhang, Cheng Li, Michael Bendersky, Marc Najork

A hybrid approach, which leverages both semantic (deep neural network-based) and lexical (keyword matching-based) retrieval models, is proposed.

Re-Ranking Retrieval

Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching

1 code implementation26 Apr 2020 Liu Yang, Mingyang Zhang, Cheng Li, Michael Bendersky, Marc Najork

In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.

Clustering Information Retrieval +9

Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking

no code implementations21 Oct 2019 Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.

Information Retrieval Learning-To-Rank +1

Domain Adaptation for Enterprise Email Search

no code implementations19 Jun 2019 Brandon Tran, Maryam Karimzadehgan, Rama Kumar Pasumarthi, Michael Bendersky, Donald Metzler

To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise.

Domain Adaptation Information Retrieval +1

Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

2 code implementations11 Nov 2018 Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork

To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.

Learning-To-Rank

Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

no code implementations15 Sep 2018 Jiaming Shen, Maryam Karimzadehgan, Michael Bendersky, Zhen Qin, Donald Metzler

In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models.

Clustering Multi-Task Learning +1

Semantic Video Trailers

no code implementations7 Sep 2016 Harrie Oosterhuis, Sujith Ravi, Michael Bendersky

Our approach effectively captures the multimodal semantics of queries and videos using state-of-the-art deep neural networks and creates a summary that is both semantically coherent and visually attractive.

Video Summarization

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