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20172025
most citedEvaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

59 citations · 158 across the 39 of their papers we have counts for

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8 papers · 1 filter

cs.CL202311 cited

On the Tool Manipulation Capability of Open-source Large Language Models

Qiantong Xu, Fenglu Hong, Bo Li +3

Recent studies on software tool manipulation with large language models (LLMs) mostly rely on closed model APIs. The industrial adoption of these models is substantially constraine…

cs.CL2023

Mixture-of-Expert Conformer for Streaming Multilingual ASR

Ke Hu, Bo Li, Tara N. Sainath +2

End-to-end models with large capacity have significantly improved multilingual automatic speech recognition, but their computation cost poses challenges for on-device applications.…

cs.CL202359 cited

Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

Bo Li, Gexiang Fang, Yang Yang +4

The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we foc…

cs.CL2023

Massively Multilingual Shallow Fusion with Large Language Models

Ke Hu, Tara N. Sainath, Bo Li +7

While large language models (LLM) have made impressive progress in natural language processing, it remains unclear how to utilize them in improving automatic speech recognition (AS…

cs.CL20232 cited

Efficient Domain Adaptation for Speech Foundation Models

Bo Li, Dongseong Hwang, Zhouyuan Huo +8

Foundation models (FMs), that are trained on broad data at scale and are adaptable to a wide range of downstream tasks, have brought large interest in the research community. Benef…

cs.CL2022

Streaming Intended Query Detection using E2E Modeling for Continued Conversation

Shuo-yiin Chang, Guru Prakash, Zelin Wu +7

In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each time…