Search Results for author:
Found 8 papers, 2 papers with code
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
Inspired by the classical word-based language modeling approach for IR, i. e., the query likelihood (QL) model, we seek to sufficiently utilize LLMs' generative ability by QL maximization.
Leveraging LLMs for Utility-Focused Annotation: Reducing Manual Effort for Retrieval and RAG
Therefore, we investigate utility-focused annotation via LLMs for large-scale retriever training data across both in-domain and out-of-domain settings on the retrieval and RAG tasks.
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Motivated by this, we propose a query-level click propensity model to capture the probability that users will click on different result lists, allowing for non-zero probabilities that users may not click on an observed relevant result.
Generative Retrieval for Book search
Directly applying GR to book search is a challenge due to the unique characteristics of book search: The model needs to retain the complex, multi-faceted information of the book, which increases the demand for labeled data.
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to obtain.
Enhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling
Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling.
Regulating the Hydrophobic Domain in Peptide-Catecholamine Coassembled Nanostructures for Fluorescence Enhancement
Hydrophobic domains provide specific microenvironment for essential functional activities in life.
MSR-net:Low-light Image Enhancement Using Deep Convolutional Network
In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed.
