On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan
2024 Uncertainty in Artificial Intelligence | July 2024
AI Chat Logs Research
AI Chat Log Research is a v-team collaboration among E+D Office of Applied Research, Microsoft Research, Bing Metrics and Analytics, and Turing to address practical challenges arising from the analysis of AI Chat logs.
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Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Longqi Yang
MSR-TR-2023-32 | September 2023
作者：Microsoft
This article is distributed under Creative Commons Attribution- NonCommercial- NoDerivatives License 4.0 (CC BY-NC-ND). https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode. Also available on arXiv at https://arxiv.org/abs/2309.13063
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
September 2023
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On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan
2024 Uncertainty in Artificial Intelligence | July 2024
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Longqi Yang
MSR-TR-2023-32 | September 2023
作者：Microsoft
This article is distributed under Creative Commons Attribution- NonCommercial- NoDerivatives License 4.0 (CC BY-NC-ND). https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode. Also available on arXiv at https://arxiv.org/abs/2309.13063
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
September 2023
-
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan
2024 Uncertainty in Artificial Intelligence | July 2024
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Longqi Yang
MSR-TR-2023-32 | September 2023
作者：Microsoft
This article is distributed under Creative Commons Attribution- NonCommercial- NoDerivatives License 4.0 (CC BY-NC-ND). https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode. Also available on arXiv at https://arxiv.org/abs/2309.13063
按类型分类的论文与出版物
-
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan
2024 Uncertainty in Artificial Intelligence | July 2024
-
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
September 2023
-
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Longqi Yang
MSR-TR-2023-32 | September 2023
作者：Microsoft
This article is distributed under Creative Commons Attribution- NonCommercial- NoDerivatives License 4.0 (CC BY-NC-ND). https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode. Also available on arXiv at https://arxiv.org/abs/2309.13063