PPT-Mitigating Hallucination for Large Language Models

Author : eleanor | Published Date : 2024-07-02

Slides by Shizhe Diao and Zoey Li Limitations An example of a hallucination ChatGPT describes the content of an article that does not exist Source Wikipedia

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Mitigating Hallucination for Large Language Models: Transcript

Slides by Shizhe Diao and Zoey Li Limitations An example of a hallucination ChatGPT describes the content of an article that does not exist Source Wikipedia Source The Harvard Gazette. WHAT ARE MITIGATING CIRCUMSTANCES 0LWLJDWLQJ57347FLUFXPVWDQFHV57347DUH57347FLUFXPVWDQFHV57347EHRQG57347D57347VWXGHQW57526V57347FRQWURO57347ZKLFK57347KDYH57347DIIHFWHG57347WKHLU57347 performance in assessments whether an examination essay practical o B00901013. 李舜仁. Outline. Introduction. Motivation. Algorithms. Future work. F. ace . H. allucination-. Outline. 1. Outline. Introduction. Motivation. Algorithms. Future work. F. ace . H. allucination-. Integrating land-surface . models with observations. By: Ben . Livneh. Overview. Background: significance of large-scale land surface modeling. Past and ongoing . projects. Importance of . linking models . CMSC 723: Computational Linguistics I ― Session #9. Jimmy Lin. The . iSchool. University of Maryland. Wednesday, October 28, 2009. N-Gram Language Models. What? . LMs assign probabilities to sequences of tokens. Data-Intensive Information Processing Applications ― Session #9. Nitin Madnani. University of Maryland. Tuesday, April 6, 2010. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. R. . Srikant. ECE/CSL. University of Illinois. A Puzzle. $5 Reward for the first correct answer. Honor code: Please don’t answer if you have heard this puzzle before. There are 10 people in a room.. Presented by Erin Palmer. What constitutes Speech Processing? . Speech processing is widely used today. Can you think of some examples?. Phone dialog systems (bank, Amtrak). Computer’s dictation feature. Conference on Empirical Methods in Natural Language . Processing 2007. 報告者：郝柏翰. 2013/06/04. Thorsten . Brants. , Ashok . C. . Popat. , . Peng. . Xu. , Franz . J. . Och. , Jeffrey Dean. Instructor: Paul Tarau, based on . Rada. . Mihalcea’s. original slides. Note. : some of the material in this slide set was adapted from an NLP course taught by Bonnie Dorr at Univ. of Maryland. Language Models. Babak Khoshnevisan. khoshnevisan@mail.usf.edu. . What do we know about idioms?. Research Questions:. What are different models and hypotheses available regarding idiomaticity?. Which hypotheses or models of idiom processing have empirically proved to be applicable for L2 contexts?. Facts. Advertisements comprise thirty percent of the material aired on television, and many of us will view more than two million commercials in our lifetimes. . The A. C. Nielson Company reports that, by the age of sixty-five, the average U.S. citizen will have spent nine years of his or her life watching television—twenty-eight hours a week, two months a year. . Lecture . 5. Albert . Gatt. LIN3022 -- Natural Language Processing. In today’s lecture. We take a look at . n-gram. . language models. Simple, probabilistic models of linguistic sequences. LIN3022 -- Natural Language Processing. Jianlin. . Jack . Cheng. Computer Science Department. University of Missouri, . Columbia, USA. Mexico, 2014. Large-Scale Model Sampling. Targeted. Sampling. Fold Space. Alignment Space. Model Pool. Sequence Space. 21In the past ten years cognitive science has seen the rapid rise of interest in models, theories of the mind based on the interaction of large numbers of simple neuron-likeprocessing units. The appr

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