Search Results for author:
Found 36 papers, 22 papers with code
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
However, a large subset of the community still uses encoder-only models for tasks such as classification or retrieval.
Rank-K: Test-Time Reasoning for Listwise Reranking
In this work, we introduce Rank-K, a listwise passage reranking model that leverages the reasoning capability of the reasoning language model at query time that provides test time scalability to serve hard queries.
MURR: Model Updating with Regularized Replay for Searching a Document Stream
The Internet produces a continuous stream of new documents and user-generated queries.
Rank1: Test-Time Compute for Reranking in Information Retrieval
We introduce Rank1, the first reranking model trained to take advantage of test-time compute.
mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval
We see strong cross-lingual performance with English-based retrievers that trained using instructions, but find a notable drop in performance in the multilingual setting, indicating that more work is needed in developing data for instruction-based multilingual retrievers.
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts.
CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
This dataset CLERC (Case Law Evaluation Retrieval Corpus), is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations (as well as previous context) into a cogent analysis that supports a reasoning goal.
Distillation for Multilingual Information Retrieval
Recent work in cross-language information retrieval (CLIR), where queries and documents are in different languages, has shown the benefit of the Translate-Distill framework that trains a cross-language neural dual-encoder model using translation and distillation.
On the Evaluation of Machine-Generated Reports
Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users.
Language Fairness in Multilingual Information Retrieval
We also evaluate real MLIR systems on two publicly available benchmarks and show that the PEER scores align with prior analytical findings on MLIR fairness.
PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval
PLAID, an efficient implementation of the ColBERT late interaction bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in monolingual, cross-language, and multilingual retrieval.
Efficiency-Effectiveness Tradeoff of Probabilistic Structured Queries for Cross-Language Information Retrieval
Probabilistic Structured Queries (PSQ) is a cross-language information retrieval (CLIR) method that uses translation probabilities statistically derived from aligned corpora.
Overview of the TREC 2023 NeuCLIR Track
The principal tasks are ranked retrieval of news in one of the three languages, using English topics.
HLTCOE at TREC 2023 NeuCLIR Track
TT trains a ColBERT model with English queries and passages automatically translated into the document language from the MS-MARCO v1 collection.
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
First, we introduce our dataset FollowIR, which contains a rigorous instruction evaluation benchmark as well as a training set for helping IR models learn to better follow real-world instructions.
Dated Data: Tracing Knowledge Cutoffs in Large Language Models
Using this analysis, we find that effective cutoffs often differ from reported cutoffs.
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation
Applying a similar knowledge distillation approach to training an efficient dual-encoder model for Cross-Language Information Retrieval (CLIR), where queries and documents are in different languages, is challenging due to the lack of a sufficiently large training collection when the query and document languages differ.
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval.
"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data.
NevIR: Negation in Neural Information Retrieval
Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR.
Synthetic Cross-language Information Retrieval Training Data
By repeating this process, collections of arbitrary size can be created in the style of MS MARCO but using naturally-occurring documents in any desired genre and domain of discourse.
Overview of the TREC 2022 NeuCLIR Track
This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval.
Defending Against Disinformation Attacks in Open-Domain Question Answering
Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems.
Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters
By adding adapters pretrained on language tasks for a specific language with task-specific adapters, prior work has shown that the adapter-enhanced models perform better than fine-tuning the entire model when transferring across languages in various NLP tasks.
When Do Decompositions Help for Machine Reading?
Answering complex questions often requires multi-step reasoning in order to obtain the final answer.
Neural Approaches to Multilingual Information Retrieval
Providing access to information across languages has been a goal of Information Retrieval (IR) for decades.
Pretrained Models for Multilingual Federated Learning
Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks.
HC4: A New Suite of Test Collections for Ad Hoc CLIR
HC4 is a new suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian, topics in English and in the document languages, and graded relevance judgments.
Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments
While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR).
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models
These models have improved the effectiveness of retrieval systems well beyond that of lexical term matching models such as BM25.
Building OCR/NER Test Collections
This means that named entities are annotated on the transcribed text.
Improving Neural Named Entity Recognition with Gazetteers
The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer.
CADET: Computer Assisted Discovery Extraction and Translation
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
Creating and Curating a Cross-Language Person-Entity Linking Collection
To stimulate research in cross-language entity linking, we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages.
