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This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 5 CONCLUSION Previous work has shown that adapting LLMs with RLHF helps them generate outputs that are preferred by humans over the supervised fine-tuned counterpart. However, recent work has also pointed out that simply training LLMs to abide by the preference of the general may result in ignoring individual preferences and values. In this work, we provide the first steps to tackle this issue by proposing Reinforcement Learning from Personalized Human Feedback as a multi-objective problem so that LLMs can be aligned to follow conflicting preferences. We propose a promising method called P-SOUPS that is able to composite models trained on single objectives on the fly during inference. We also highlight the scalability of P-SOUPS by showing that it scales linearly, instead of exponentially like the MORL baseline, with regards to the number of new preferences, which is required to provide true personalization to individual users. ACKNOWLEDGMENTS Thanks to Minyoung Hwang, Sungdong Kim, Tim Dettmers, Yoonjoo Lee, and Margaret Li for helpful feedback and discussion. REFERENCES Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie. PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 82–92, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.7. 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We also provide 3 demonstrations: one scenario where there is a tie because both responses do not contain any notion of the preference (e.g. both responses do not show any signs of friendliness), one scenario where there is a clear winner, and one scenario where they are both good, but one is better than the other. Simulated pairwise evaluation We recruited 24 crowd workers for our human evaluation. Figure 5 shows the interface used for human evaluation. We consider both the ‘Tie’ and ‘Both are bad’ options to be Ties. B CRITERIA-WISE EVALUATION The criteria-wise win rate (%) across all the methods are shown in Figure 6. The criteria-wise win rate was calculated by getting the average win-rate of the preference combinations that contained the specific preference dimension. For example, when calculating the criteria-wise win rate of ‘Elementary’, we got the average win-rate of the preference combinations that contained the ‘Elementary’ preference, which includes AAA, AAB, ABA, and ABB. C THE FULL LIST OF EVALUATION PROMPTS The full list of evaluation prompts used in our experiments are provided in Table 5 D DETAILED RESULTS FOR HUMAN EVALUATION AND GPT-4 EVALUATION We provide detailed results (win / loss / tie for each of the preference combinations for our main experimental results. Table 6 shows the GPT4 evaluation and Table 7 shows the human evaluation results. E EXAMPLES OF P-SOUPS TEXT GENERATIONS Table 8 shows empirical examples of the text generated from each preference combination of the 16 preference combination experiments for the same prompt.

Personalized Soups: LLM Alignment Via Parameter Merging - Conclusion & References

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dihia LANASRI, OMDENA, New York, USA; (2) Juan OLANO, OMDENA, New York, USA; (3) Sifal KLIOUI, OMDENA, New York, USA; (4) Sin Liang Lee, OMDENA, New York, USA; (5) Lamia SEKKAI, OMDENA, New York, USA. Table of Links Abstract & Introduction Background Related Work Our Methodology Experiments and Results Conclusion, Acknowledgments & References 6 Conclusion The importance of hate speech detection on social networks has encouraged many researchers to build solutions (corpora and classifiers) to detect suspect messages. The literature review shows that most works are interested in text in structured languages like English, French, Arabic, etc. However, few works deal with dialects, mainly the Algerian one, which is known for its complexity and variety. To fill in the gap, we propose in this paper a complete NLP approach to detect hate speech in the Algerian dialect. We built an annotated corpus of more than 13,5K documents, which is used to evaluate various deep learning architectures. The obtained results are very promising, where the most accurate was the DzaraShield . Looking ahead, there is significant potential to enhance inference speed, particularly for the Dziribert-based and multilingual models. While this project primarily focused on Arabic characters, our next step will be to address the dialect when written in Latin characters. Embracing both Arabic and Latin characters will more accurately capture the nuances of the written Algerian dialect. Finally, we plan to expand our corpus size and explore alternative deep-learning architectures. 7 Acknowledgments We would like to thank every person who has contributed to this project: Micha Freidin, Viktor Ivanenko, Piyush Aaryan, Yassine Elboustani, Tasneem Elyamany, Cephars Bonacci, Nolan Wang and Lydia Khelifa Chibout. 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Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. E Moatez Billah Nagoudi, A Elmadany, and M Abdul-Mageed. Arat5: Text-to-text transformers for arabic language understanding and generation. arXiv preprint arXiv:2109.12068, 2021.

Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion,  Acknowledgments

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 9 CONCLUSION This paper presents ConstitutionMaker, a tool for interactively refining LLM outputs by converting users’ intuitive feedback into principles. ConstitutionMaker’s design is informed by a formative study, where we also collected and classified the types of principles users wanted to write. ConstitutionMaker incorporates three principle-elicitation features: kudos, critique, and rewrite. In a user study with 14 industry professionals, participants felt that ConstitutionMaker helped them (1) write principles that effectively guided the chatbot, (2) convert their feedback into principles more easily, and (3) write principles more efficiently, with (4) less mental demand than the baseline. This was due to ConstitutionMaker supporting their thought processes, including helping them to: identify ways to improve the bot’s responses, convert their intuition into verbal feedback, and phrase their feedback as specific principles. There are many avenues of future work, including supporting users in iterating on and clarifying their principles, organizing larger sets of principles and supporting multiple writers, and helping users test chatbots across multiple user journeys. Together, these findings inform future tools that support interactively customizing LLM outputs. REFERENCES [1] Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy TelleenLawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022. Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073 [cs.CL] [2] Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012. 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Interactively Critiquing LLMs by Converting Feedback into Principle— Conclusion and References

Gemini - A Family of Highly Capable Multimodal Models: Discussion and Conclusion, References

Lasers by Hal Hellman, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CONTROLLED EMISSION here CONTROLLED EMISSION The laser and its parent, the maser, can be traced back half a century to its theoretical beginnings. The great physicist Albert Einstein is most widely known for his work in relativity. But he did early and important work on that other gigantic 20th century scientific achievement, the quantum theory. In one of his papers, published first in Zurich, Switzerland, in 1916, Einstein showed that controlled emission of light energy could be obtained from an atom under certain conditions. When an atom or molecule has somehow had its energy level raised, the release of this stored energy could be stimulated by subjecting the atom or molecule to a small “shot” of electromagnetic radiation of the proper frequency. Einstein wrote that when such a photon of energy caused an electron to drop from a higher to a lower orbit, the electron would emit another photon of the same frequency and in the same direction as the one that hit it. In other words, the energy of the emitted photon would be added to that of the photon that stimulated the emission in the first place. Here, , was light amplification. The three major factors, absorption of energy, spontaneous emission, and stimulated emission are diagrammed in . potentially Figure 14 There the matter lay for more than 30 years. In 1951 Charles H. Townes, then on the Columbia University faculty, was interested in ways of extending to still higher frequencies the range of microwaves available for use in communications and in other scientific applications. Townes and other scientists who were interested in the problem were to meet in Washington, D. C., on the 26th of April. The night before the meeting he slept in a small Washington hotel; but he awoke at 5:30—pondering, pondering the high frequency problem. He dressed and took a walk, then sat on a park bench and savored the beauty of azaleas in bloom. But all the while his mind was running over the various aspects of the problem. Figure 14 An atom can release absorbed energy spontaneously or it can be stimulated to do so. Suddenly the answer came to him. Normally more of the molecules in any substance are in low-energy states than in high ones. He would change the natural balance and create a situation with an abnormally large number of high-energy molecules. Then he would stimulate them to emit their energy by nudging them with microwaves. Here was amplification. He could even take some of the emitted radiation and feed it back into the device to stimulate additional molecules, thereby creating an oscillator. This arrangement, he knew, could be carried out in a cavity, which would resonate (just like an organ pipe) at the proper frequency. The resonator would be a box whose dimensions were comparable with the wavelength of the radiation, that is, a few centimeters on a side. feedback On the back of an envelope he figured out some of the basic requirements. Three years, and many experiments, later the maser ( icrowave mplification by timulated mission of adiation) was a reality. The original maser was a small metal box into which excited ammonia molecules were added. When microwaves were beamed into the excited ammonia the box emitted a pure, strong beam of high frequency microwaves, far more temporally coherent than any that had ever been achieved before. The output of an ammonia maser is stable to one part in 100 billion, making it an extremely accurate atomic “clock”. Moreover, the amplifying properties of masers have been found to be very useful for magnifying faint radio signals from space, and for satellite communications. m a s e r Ammonia gas was chosen for the first maser because molecules of ammonia have two individual energy states that are separated by a gap corresponding in frequency to 23,870 megacycles (23,870 million cycles) per second. Ammonia molecules also react to a nonuniform electric field in ways that depend on their energy level: low-level molecules can be attracted and high-level ones repelled by the same field. Thus it is possible to separate the low-energy molecules from the high, and to get the excited molecules into the cavity without too much trouble. This procedure for getting the majority of atoms or molecules in a container into a higher energy state, is called and is basic to the operation of both masers and lasers. population inversion It should be noted that two Russians, N. G. Basov and A. M. Prokhorov, were working along similar lines independently of Townes. In 1952 they presented a paper at an All-Union (U.S.S.R.) Conference, in which they discussed the possibility of constructing a “molecular generator”, that is, a maser. Their proposal, first published in 1954, was in many respects similar to Townes’s. In 1955, Basov and Prokhorov discussed, in a short note, a new way to obtain the active atomic systems for a maser, a method that turned out to be of great importance. Thus on October 29, 1964, the Nobel Prize in Physics was awarded, not only to Townes, but to Basov and Prokhorov as well. The award was for fundamental work in the field of quantum electronics, which has led to the construction of oscillators and amplifiers based on the “aser” principle. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Hal Hellman (2021). Laser. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/65512/pg65512-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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