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Found 45 papers, 23 papers with code
QUITO-X: An Information Bottleneck-based Compression Algorithm with Cross-Attention
Generative LLM have achieved significant success in various industrial tasks and can effectively adapt to vertical domains and downstream tasks through ICL.
QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression
Specifically, we take a trigger token to calculate the attention distribution of the context in response to the question.
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval
Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query.
Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks.
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
However, limiting perturbations to a single level of granularity may reduce the flexibility of adversarial examples, thereby diminishing the potential threat of the attack.
Are Large Language Models Good at Utility Judgments?
Retrieval-augmented generation (RAG) is considered to be a promising approach to alleviate the hallucination issue of large language models (LLMs), and it has received widespread attention from researchers recently.
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Very recently, a pre-trained generative retrieval model for KILTs, named CorpusBrain, was proposed and reached new state-of-the-art retrieval performance.
RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication.
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently.
From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence.
Continual Learning for Generative Retrieval over Dynamic Corpora
We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR: (i) To encode new documents into docids with low computational cost, we present Incremental Product Quantization, which updates a partial quantization codebook according to two adaptive thresholds; and (ii) To memorize new documents for querying without forgetting previous knowledge, we propose a memory-augmented learning mechanism, to form meaningful connections between old and new documents.
L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations
First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection.
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method
The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query.
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models
In this paper, we focus on a more general type of perturbation and introduce the topic-oriented adversarial ranking attack task against NRMs, which aims to find an imperceptible perturbation that can promote a target document in ranking for a group of queries with the same topic.
Visual Named Entity Linking: A New Dataset and A Baseline
Since each entity often contains rich visual and textual information in KBs, we thus propose three different sub-tasks, i. e., visual to visual entity linking (V2VEL), visual to textual entity linking (V2TEL), and visual to visual-textual entity linking (V2VTEL).
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models
A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack.
Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models
When sampling top-ranked results (excluding the labeled positives) as negatives from a stronger retriever, the performance of the learned NRM becomes even worse.
Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval
Unlike the promising results in NLP, we find that these methods cannot achieve comparable performance to full fine-tuning at both stages when updating less than 1\% of the original model parameters.
A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval
Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval.
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.
Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction
% Therefore, in this work, we propose to drop out the decoder and introduce a novel contrastive span prediction task to pre-train the encoder alone.
GERE: Generative Evidence Retrieval for Fact Verification
This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead; ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking; iii) a fixed number of sentences are selected to form the final evidence set.
PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models
We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list.
A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty
By reducing the weights of the majority classes, such instances would become more difficult to learn and hurt the overall performance consequently.
Pre-training Methods in Information Retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need.
Are Neural Ranking Models Robust?
So we raise the question in this work: Are neural ranking models robust?
FedMatch: Federated Learning Over Heterogeneous Question Answering Data
A possible solution to this dilemma is a new approach known as federated learning, which is a privacy-preserving machine learning technique over distributed datasets.
A Discriminative Semantic Ranker for Question Retrieval
However, these methods often lose the discriminative power as term-based methods, thus introduce noise during retrieval and hurt the recall performance.
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval
The basic idea of PROP is to construct the \textit{representative words prediction} (ROP) task for pre-training inspired by the query likelihood model.
Semantic Models for the First-stage Retrieval: A Comprehensive Review
We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development.
A Linguistic Study on Relevance Modeling in Information Retrieval
We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question.
Learning to Truncate Ranked Lists for Information Retrieval
One is the widely adopted metric such as F1 which acts as a balanced objective, and the other is the best F1 under some minimal recall constraint which represents a typical objective in professional search.
PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Recently pre-trained language representation models such as BERT have shown great success when fine-tuned on downstream tasks including information retrieval (IR).
Continual Domain Adaptation for Machine Reading Comprehension
To tackle such a challenge, in this work, we introduce the \textit{Continual Domain Adaptation} (CDA) task for MRC.
Query Understanding via Intent Description Generation
To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query.
Match$^2$: A Matching over Matching Model for Similar Question Identification
However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i. e., there could be different ways to ask a same question or different questions sharing similar expressions.
Outline Generation: Understanding the Inherent Content Structure of Documents
To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings.
Controlling Risk of Web Question Answering
Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need.
MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation.
A Deep Look into Neural Ranking Models for Information Retrieval
Ranking models lie at the heart of research on information retrieval (IR).
Learning to Control the Specificity in Neural Response Generation
In conversation, a general response (e. g., {``}I don{'}t know{''}) could correspond to a large variety of input utterances.
Modeling Diverse Relevance Patterns in Ad-hoc Retrieval
The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document.
A Deep Relevance Matching Model for Ad-hoc Retrieval
Specifically, our model employs a joint deep architecture at the query term level for relevance matching.
Ranked #14 on
Ad-Hoc Information Retrieval
on TREC Robust04
MatchZoo: A Toolkit for Deep Text Matching
In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.
