Search Results for author: Tianshu Wang

Found 5 papers, 3 papers with code

Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering

1 code implementation16 Apr 2024 Xiaoyang Chen, Ben He, Hongyu Lin, Xianpei Han, Tianshu Wang, Boxi Cao, Le Sun, Yingfei Sun

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.

Information Retrieval Language Modelling +3

DBCopilot: Scaling Natural Language Querying to Massive Databases

1 code implementation6 Dec 2023 Tianshu Wang, Hongyu Lin, Xianpei Han, Le Sun, Xiaoyang Chen, Hao Wang, Zhenyu Zeng

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions into Structured Query Language (SQL) queries.

Navigate Question Generation +2

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models

no code implementations16 May 2023 Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities.

World Knowledge

Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

no code implementations12 May 2022 Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han, Le Sun, Feiyu Xiong, Hui Chen, Minlong Lu, Xiuwen Zhu

Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.

Entity Resolution

Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy

1 code implementation27 Sep 2021 Tianshu Wang, Peter Melchior

But many concrete allocation problems in the experimental and observational sciences cannot or should not be expressed in the form of linear objective functions.

POS

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