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Found 28 papers, 5 papers with code
Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results.
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
Our method takes both LLM generated relevance labels and pairwise preferences.
Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers
We first show that directly applying the expansion techniques in the current literature to state-of-the-art neural rankers can result in deteriorated zero-shot performance.
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.
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.
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.
Query Expansion by Prompting Large Language Models
Query expansion is a widely used technique to improve the recall of search systems.
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.
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.
Rank4Class: A Ranking Formulation for Multiclass Classification
Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes.
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.
Improving Neural Ranking via Lossless Knowledge Distillation
We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized identically to their teachers.
Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank
We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.
Distilling Interpretable Models into Human-Readable Code
The goal of model distillation is to faithfully transfer teacher model knowledge to a model which is faster, more generalizable, more interpretable, or possesses other desirable characteristics.
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees
We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.
What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education
Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education.
RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble
In this paper, we report the results of our participation in the TREC-COVID challenge.
Adaptive Double-Exploration Tradeoff for Outlier Detection
We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold.
Interpretable Learning-to-Rank with Generalized Additive Models
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area.
Separate and Attend in Personal Email Search
In personal email search, user queries often impose different requirements on different aspects of the retrieved emails.
Spherical Text Embedding
While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding.
Identifying Outlier Arms in Multi-Armed Bandit
Outlier detection is a powerful method to narrow down the attention to a few objects after the data for them are collected.
Identifying Semantically Deviating Outlier Documents
A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus.
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods.
