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Found 25 papers, 11 papers with code
Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
While well-trained text detectors have demonstrated promising performance on unseen test data, recent research suggests that these detectors have vulnerabilities when dealing with adversarial attacks such as paraphrasing.
Self-Retrieval: Building an Information Retrieval System with One Large Language Model
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning.
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
This paper aims to bridge this gap by constructing AIG-ASAP, an AI-generated student essay dataset, employing a range of text perturbation methods that are expected to generate high-quality essays while evading detection.
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs).
Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval
Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process.
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection
Neural ranking models (NRMs) have undergone significant development and have become integral components of information retrieval (IR) systems.
Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
Understanding Differential Search Index for Text Retrieval
The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in response to a given query.
Towards Imperceptible Document Manipulations against Neural Ranking Models
Additionally, current methods rely heavily on the use of a well-imitated surrogate NRM to guarantee the attack effect, which makes them difficult to use in practice.
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
(4) Can ChatGPT effectively leverage commonsense for answering questions?
Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective
Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph.
Groupwise Query Performance Prediction with BERT
While large-scale pre-trained language models like BERT have advanced the state-of-the-art in IR, its application in query performance prediction (QPP) is so far based on pointwise modeling of individual queries.
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
In this paper, we propose a new framework for unsupervised MRC.
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently.
Global Bootstrapping Neural Network for Entity Set Expansion
Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision.
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Query expansion aims to mitigate the mismatch between the language used in a query and in a document.
PARADE: Passage Representation Aggregation for Document Reranking
In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.
Ranked #2 on
Ad-Hoc Information Retrieval
on TREC Robust04
Learning to Bootstrap for Entity Set Expansion
Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category.
Image Captioning based on Deep Learning Methods: A Survey
Image captioning is a challenging task and attracting more and more attention in the field of Artificial Intelligence, and which can be applied to efficient image retrieval, intelligent blind guidance and human-computer interaction, etc.
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.
Ranked #9 on
Ad-Hoc Information Retrieval
on TREC Robust04
TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring
Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data.
