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Found 16 papers, 8 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.
Text Data-Centric Image Captioning with Interactive Prompts
Among them, the mainstream solution is to project image embeddings into the text embedding space with the assistance of consistent representations between image-text pairs from the CLIP model.
Academically intelligent LLMs are not necessarily socially intelligent
The academic intelligence of large language models (LLMs) has made remarkable progress in recent times, but their social intelligence performance remains unclear.
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.
AI for social science and social science of AI: A Survey
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities.
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.
DLUE: Benchmarking Document Language Understanding
Understanding documents is central to many real-world tasks but remains a challenging topic.
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.
End-to-End Transformer Based Model for Image Captioning
Firstly, we adopt SwinTransformer to replace Faster R-CNN as the backbone encoder to extract grid-level features from given images; Then, referring to Transformer, we build a refining encoder and a decoder.
ECO v1: Towards Event-Centric Opinion Mining
Events are considered as the fundamental building blocks of the world.
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.
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
A Survey on Neural Machine Reading Comprehension
Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge.
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
