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Found 34 papers, 19 papers with code
An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering
The bi-encoder design of dense passage retriever (DPR) is a key factor to its success in open-domain question answering (QA), yet it is unclear how DPR’s question encoder and passage encoder individually contributes to overall performance, which we refer to as the encoder attribution problem.
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval
Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements.
Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning
In particular, we find that Rank-R1 achieves effectiveness on in-domain datasets at par with that of supervised fine-tuning methods, but utilizing only 18\% of the training data used by the fine-tuning methods.
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers
In particular, we adopt pruned LLMs as the backbone and train on diverse LLM-augmented data in a single-stage contrastive learning setup.
PixelWorld: Towards Perceiving Everything as Pixels
Existing foundation models typically process visual input as pixels and textual input as tokens, a paradigm that contrasts with human perception, where both modalities are processed in a unified manner.
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vectors to enable effective search and offer a simplified pipeline over traditional text-only methods.
VISA: Retrieval Augmented Generation with Visual Source Attribution
Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems.
Unifying Multimodal Retrieval via Document Screenshot Embedding
To this end, we propose Document Screenshot Embedding (DSE), a novel retrieval paradigm that regards document screenshots as a unified input format, which does not require any content extraction preprocess and preserves all the information in a document (e. g., text, image and layout).
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains.
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: (1) prompt-based re-ranking methods, which require no further training but are only feasible for re-ranking a handful of candidate documents due to computational costs; and (2) unsupervised contrastive trained dense retrieval methods, which can retrieve relevant documents from the entire corpus but require a large amount of paired text data for contrastive training.
Fine-Tuning LLaMA for Multi-Stage Text Retrieval
Our findings demonstrate that the effectiveness of large language models indeed surpasses that of smaller models.
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking.
Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (Published in Findings of EMNLP 2024)
In this study, we present a system called LLMs Augmented with Medical Textbooks (LLM-AMT) designed to enhance the proficiency of LLMs in specialized domains.
Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard
BEIR is a benchmark dataset for zero-shot evaluation of information retrieval models across 18 different domain/task combinations.
TheoremQA: A Theorem-driven Question Answering dataset
We evaluate a wide spectrum of 16 large language and code models with different prompting strategies like Chain-of-Thoughts and Program-of-Thoughts.
Ranked #1 on
Natural Questions
on TheoremQA
Zero-Shot Listwise Document Reranking with a Large Language Model
Supervised ranking methods based on bi-encoder or cross-encoder architectures have shown success in multi-stage text ranking tasks, but they require large amounts of relevance judgments as training data.
Few-shot In-context Learning for Knowledge Base Question Answering
On GrailQA and WebQSP, our model is also on par with other fully-trained models.
Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes
With Pyserini, which provides a Python interface to Anserini, users gain access to both sparse and dense retrieval models, as Pyserini implements bindings to the Faiss vector search library alongside Lucene inverted indexes in a uniform, consistent interface.
SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes
Multi-vector retrieval methods have demonstrated their effectiveness on various retrieval datasets, and among them, ColBERT is the most established method based on the late interaction of contextualized token embeddings of pre-trained language models.
Precise Zero-Shot Dense Retrieval without Relevance Labels
Given a query, HyDE first zero-shot instructs an instruction-following language model (e. g. InstructGPT) to generate a hypothetical document.
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
By combining PoT with self-consistency decoding, we can achieve SoTA performance on all math problem datasets and near-SoTA performance on financial datasets.
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
In this paper we consider the problem of combining the relevance signals from sparse and dense retrievers in the context of Pseudo Relevance Feedback (PRF).
Towards Best Practices for Training Multilingual Dense Retrieval Models
Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research.
Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval
In this paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency, flexibility, and code simplicity.
Sparsifying Sparse Representations for Passage Retrieval by Top-$k$ Masking
Sparse lexical representation learning has demonstrated much progress in improving passage retrieval effectiveness in recent models such as DeepImpact, uniCOIL, and SPLADE.
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Finally, we contribute a study of the generalisability of the ANCE-PRF method when dense retrievers other than ANCE are used for the first round of retrieval and for encoding the PRF signal.
Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations.
A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques
Recent developments in representational learning for information retrieval can be organized in a conceptual framework that establishes two pairs of contrasts: sparse vs. dense representations and unsupervised vs. learned representations.
A Replication Study of Dense Passage Retriever
Text retrieval using learned dense representations has recently emerged as a promising alternative to "traditional" text retrieval using sparse bag-of-words representations.
Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations
Pyserini is an easy-to-use Python toolkit that supports replicable IR research by providing effective first-stage retrieval in a multi-stage ranking architecture.
Cultural Vocal Bursts Intensity Prediction
Information Retrieval
+1
Scientific Claim Verification with VERT5ERINI
This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain.
