Search Results for author:
Found 22 papers, 9 papers with code
Gecko: Versatile Text Embeddings Distilled from Large Language Models
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers?
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance.
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
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.
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem.
How Does Generative Retrieval Scale to Millions of Passages?
Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer.
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT.
Retrieval Augmentation for T5 Re-ranker using External Sources
Retrieval augmentation has shown promising improvements in different tasks.
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
This results in significant inference time speedups since the decoder-only architecture only needs to learn to interpret static encoder embeddings during inference.
Transformer Memory as a Differentiable Search Index
In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model.
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training.
Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing
In this work, we collect judgments from multiple judges using a crowdsourcing platform and aggregate them to compare the two kinds of preference judgments in terms of transitivity, time consumption, and quality.
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.
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.
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.
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.
DE-PACRR: Exploring Layers Inside the PACRR Model
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval.
PACRR: A Position-Aware Neural IR Model for Relevance Matching
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query.
