Search Results for author:
Found 12 papers, 8 papers with code
Semiparametric Token-Sequence Co-Supervision
Especially, the robustness of parametric token space which is established during the pretraining step tends to effectively enhance the stability of nonparametric sequence embedding space, a new space established by another language model.
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
Enhancing the capability of retrievers to understand intentions and preferences of users, akin to language model instructions, has the potential to yield more aligned search targets.
Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders
Prevailing research practice today often relies on training dense retrievers on existing large datasets such as MSMARCO and then experimenting with ways to improve zero-shot generalization capabilities to unseen domains.
How Well Do Large Language Models Truly Ground?
Reliance on the inherent knowledge of Large Language Models (LLMs) can cause issues such as hallucinations, lack of control, and difficulties in integrating variable knowledge.
KTRL+F: Knowledge-Augmented In-Document Search
We introduce a new problem KTRL+F, a knowledge-augmented in-document search task that necessitates real-time identification of all semantic targets within a document with the awareness of external sources through a single natural query.
Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment
This is accomplished by introducing a soft moment mask that represents a temporal segment in the video and jointly optimizing it with the prefix parameters of a language model.
Local 3D Editing via 3D Distillation of CLIP Knowledge
Specifically, we present three add-on modules of LENeRF, the Latent Residual Mapper, the Attention Field Network, and the Deformation Network, which are jointly used for local manipulations of 3D features by estimating a 3D attention field.
Exploring the Practicality of Generative Retrieval on Dynamic Corpora
Benchmarking the performance of information retrieval (IR) methods are mostly conducted with a fixed set of documents (static corpora); in realistic scenarios, this is rarely the case and the document to be retrieved are constantly updated and added.
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal prompt among the candidates for a given task but have failed to provide a comprehensive and fair comparison between each other.
Nonparametric Decoding for Generative Retrieval
The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed.
Generative Multi-hop Retrieval
A common practice for text retrieval is to use an encoder to map the documents and the query to a common vector space and perform a nearest neighbor search (NNS); multi-hop retrieval also often adopts the same paradigm, usually with a modification of iteratively reformulating the query vector so that it can retrieve different documents at each hop.
Cost-effective End-to-end Information Extraction for Semi-structured Document Images
A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost.
