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Found 19 papers, 11 papers with code
Speech Aerodynamics Database, Tools and Visualisation
Aerodynamic processes underlie the characteristics of the acoustic signal of speech sounds.
Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression
It finetunes the compression plugin module and uses the representations of gist tokens to emulate the raw prompts in the vanilla language model.
ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
Retrieval Augmented Generation (RAG) has introduced a new paradigm for Large Language Models (LLMs), aiding in the resolution of knowledge-intensive tasks.
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In
Retrieval augmentation can aid language models (LMs) in knowledge-intensive tasks by supplying them with external information.
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Common IR pipelines are typically cascade systems that may involve multiple rankers and/or fusion models to integrate different information step-by-step.
Rethinking Dense Retrieval's Few-Shot Ability
Moreover, the dataset is disjointed into base and novel classes, allowing DR models to be continuously trained on ample data from base classes and a few samples in novel classes.
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training.
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning
Machine Learning (ML) has been embraced as a powerful tool by the financial industry, with notable applications spreading in various domains including investment management.
Few-Shot Conversational Dense Retrieval
In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products.
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning
Query reformulation aims to alter noisy or ambiguous text sequences into coherent ones closer to natural language questions.
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks.
Learning Risk Preferences from Investment Portfolios Using Inverse Optimization
Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data.
Few-Shot Generative Conversational Query Rewriting
Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing information retrieval systems.
Eigendecomposition of Q in Equally Constrained Quadratic Programming
When applying eigenvalue decomposition on the quadratic term matrix in a type of linear equally constrained quadratic programming (EQP), there exists a linear mapping to project optimal solutions between the new EQP formulation where $Q$ is diagonalized and the original formulation.
A Financial Service Chatbot based on Deep Bidirectional Transformers
Our main novel contribution is the discussion about uncertainty measure for BERT, where three different approaches are systematically compared on real problems.
