Search Results for author:
Found 30 papers, 6 papers with code
Integrating Planning into Single-Turn Long-Form Text Generation
To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles.
Inference Scaling for Long-Context Retrieval Augmented Generation
Our observations reveal that increasing inference computation leads to nearly linear gains in RAG performance when optimally allocated, a relationship we describe as the inference scaling laws for RAG.
LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
We introduce LAMPO, a novel paradigm that leverages Large Language Models (LLMs) for solving few-shot multi-class ordinal classification tasks.
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I
In this work, we propose two methods based on prediction-powered inference and conformal risk control that utilize computer-generated relevance annotations to place reliable confidence intervals (CIs) around IR evaluation metrics.
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
Our method takes both LLM generated relevance labels and pairwise preferences.
LiPO: Listwise Preference Optimization through Learning-to-Rank
In this work, we formulate the LM alignment as a \textit{listwise} ranking problem and describe the LiPO framework, where the policy can potentially learn more effectively from a ranked list of plausible responses given the prompt.
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.
Learning to Rank when Grades Matter
In this paper, we conduct a rigorous study of learning to rank with grades, where both ranking performance and grade prediction performance are important.
LibAUC: A Deep Learning Library for X-Risk Optimization
This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks.
Query Expansion by Prompting Large Language Models
Query expansion is a widely used technique to improve the recall of search systems.
Metric-agnostic Ranking Optimization
To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in \textit{Metric-agnostic Ranking Optimization}.
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.
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design.
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.
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.
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.
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback.
Interpretable Learning-to-Rank with Generalized Additive Models
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area.
Learning-to-Rank with BERT in TF-Ranking
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.
Multi-Faceted Ranking of News Articles using Post-Read Actions
Our findings show that it is possible to incorporate post-read signals that are commonly available on online news sites to improve quality of recommendations.
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
\emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature.
