Search Results for author:
Found 26 papers, 18 papers with code
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers.
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
In this paper, we investigate various aspects of embedding models that could influence the recoverability of text using Vec2Text.
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent.
Large Language Models for Stemming: Promises, Pitfalls and Failures
With this respect, we identify three avenues, each characterised by different trade-offs in terms of computational cost, effectiveness and robustness : (1) use LLMs to stem the vocabulary for a collection, i. e., the set of unique words that appear in the collection (vocabulary stemming), (2) use LLMs to stem each document separately (contextual stemming), and (3) use LLMs to extract from each document entities that should not be stemmed, then use vocabulary stemming to stem the rest of the terms (entity-based contextual stemming).
Leveraging LLMs for Unsupervised Dense Retriever Ranking
Existing methodologies for ranking dense retrievers fall short in addressing these domain shift scenarios.
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
Our ReSLLM method exploits LLMs to drive the selection of resources in federated search in a zero-shot setting.
Zero-shot Generative Large Language Models for Systematic Review Screening Automation
Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions.
Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models
We describe team ielab from CSIRO and The University of Queensland's approach to the 2023 TREC Clinical Trials Track.
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document.
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Our approach reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, significantly improving the efficiency of LLM-based zero-shot ranking.
Selecting which Dense Retriever to use for Zero-Shot Search
We propose the new problem of choosing which dense retrieval model to use when searching on a new collection for which no labels are available, i. e. in a zero-shot setting.
Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search.
Exploring the Representation Power of SPLADE Models
The SPLADE (SParse Lexical AnD Expansion) model is a highly effective approach to learned sparse retrieval, where documents are represented by term impact scores derived from large language models.
Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval
Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task.
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel re-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks.
AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents
On the basis of these needs we release an information retrieval test collection comprising real questions, a large collection of scientific documents split in passages, and ground truth relevance assessments indicating which passages are relevant to each question.
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages.
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).
CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos
We then demonstrate that the root cause of this resides in the input tokenization strategy employed by BERT.
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
We then exploit such historic implicit interactions to improve the effectiveness of a DR. A key challenge that we study is the effect that biases in the click signal, such as position bias, have on the DRs.
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training
A simple and efficient strategy to validate deep learning checkpoints is the addition of validation loops to execute during training.
Reinforcement Online Learning to Rank with Unbiased Reward Shaping
Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived from users' interactions, such as clicks.
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.
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets.
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical especially under resource constraints.
