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Found 19 papers, 5 papers with code
Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization
We show that SAMMO generalizes previous methods and improves the performance of complex prompts on (1) instruction tuning, (2) RAG pipeline tuning, and (3) prompt compression, across several different LLMs.
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation.
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry.
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation
We present a TrainRec, a lightweight and flexible toolkit for offline training and evaluation of recommender systems that implements these guidelines.
EvalRS: a Rounded Evaluation of Recommender Systems
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces.
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i. e., users and items) is available upfront at training time.
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level).
Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity.
Deep Generalized Method of Moments for Instrumental Variable Analysis
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible.
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers
In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies.
Unbiased Comparative Evaluation of Ranking Functions
Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge.
Online Updating of Word Representations for Part-of-Speech Tagging
We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible.
Online unsupervised domain adaptation
Part-Of-Speech Tagging
+2
Recommendations as Treatments: Debiasing Learning and Evaluation
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself.
Towards a Better Understanding of Predict and Count Models
In a recent paper, Levy and Goldberg pointed out an interesting connection between prediction-based word embedding models and count models based on pointwise mutual information.
Using Shortlists to Support Decision Making and Improve Recommender System Performance
From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality.
FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging
We present FLORS, a new part-of-speech tagger for domain adaptation.
