Search Results - "Henighan, Tom"

Phonon spectroscopy with sub-meV resolution by femtosecond x-ray diffuse scattering by Zhu, Diling, Robert, Aymeric, Henighan, Tom, Lemke, Henrik T., Chollet, Matthieu, Glownia, J. Mike, Reis, David A., Trigo, Mariano

“…We present a reconstruction of the transverse acoustic phonon dispersion of germanium from femtosecond time-resolved x-ray diffuse scattering measurements at…”
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Doom Lake holiday by Henighan, Tom

“…A summer holiday turns into a nightmare when the Mallory family rents a shabby cottage in a remote corner of Ontario's Rideau Lakes…”
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Demon in my view by Henighan, Tom

“…The year is 2099. North America is in chaos, ruined by years of war, terrorism, and ecological destruction. Young Toby Johnson lives with his father, one of…”
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Viking terror by Henighan, Tom

“…When 17-year-old Rigg and his friend Ari hang a marauding wolf in the wilds of medieval Greenland, they get much more than they bargained for: a hint of…”
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Mercury man by Henighan, Tom

“…Tom Blake, likeable but shy high school student, fantasizes about adventure, romance, and discovering "portholes" to the past. Little does he know that all are…”
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Couplings of Phonons to Light and One Another Studied with LCLS by Henighan, Tom

“…Engineering the thermal properties of materials plays a crucial role in technologies including propulsion, computing, optics, and household appliances. Lattice…”
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Anomalous two-photon Compton scattering by Kettle, B, Aquila, A, Boutet, S, Bucksbaum, P H, Carini, G, Feng, Y, Gamboa, E, Ghimire, S, Glenzer, S, Hart, P, Hastings, J B, Henighan, T, Hunter, M, Koglin, J, Kozina, M, Liu, H, MacDonald, M J, Trigo, M, Reis, D A, Fuchs, M

“…Abstract X-ray free-electron lasers can generate radiation pulses with extreme peak intensities at short wavelengths. This enables the investigation of…”
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Viking quest by Henighan, Tom, Carroll, Michael

“…Fifteen-year-old Rigg, son of Leif Ericsson, loves mystery and adventure. In the early eleventh century, the boy finds both of these in abundance when his…”
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Scaling Laws for Transfer by Hernandez, Danny, Kaplan, Jared, Henighan, Tom, McCandlish, Sam

“…We study empirical scaling laws for transfer learning between distributions in an unsupervised, fine-tuning setting. When we train increasingly large neural…”
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Toy Models of Superposition by Elhage, Nelson, Hume, Tristan, Olsson, Catherine, Schiefer, Nicholas, Henighan, Tom, Kravec, Shauna, Hatfield-Dodds, Zac, Lasenby, Robert, Drain, Dawn, Chen, Carol, Grosse, Roger, McCandlish, Sam, Kaplan, Jared, Amodei, Dario, Wattenberg, Martin, Olah, Christopher

“…Neural networks often pack many unrelated concepts into a single neuron - a puzzling phenomenon known as 'polysemanticity' which makes interpretability much…”
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Specific versus General Principles for Constitutional AI by Kundu, Sandipan, Bai, Yuntao, Kadavath, Saurav, Askell, Amanda, Callahan, Andrew, Chen, Anna, Goldie, Anna, Balwit, Avital, Mirhoseini, Azalia, McLean, Brayden, Olsson, Catherine, Evraets, Cassie, Tran-Johnson, Eli, Durmus, Esin, Perez, Ethan, Kernion, Jackson, Kerr, Jamie, Ndousse, Kamal, Nguyen, Karina, Elhage, Nelson, Cheng, Newton, Schiefer, Nicholas, DasSarma, Nova, Rausch, Oliver, Larson, Robin, Yang, Shannon, Kravec, Shauna, Telleen-Lawton, Timothy, Liao, Thomas I, Henighan, Tom, Hume, Tristan, Hatfield-Dodds, Zac, Mindermann, Sören, Joseph, Nicholas, McCandlish, Sam, Kaplan, Jared

“…Human feedback can prevent overtly harmful utterances in conversational models, but may not automatically mitigate subtle problematic behaviors such as a…”
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Scaling Laws and Interpretability of Learning from Repeated Data by Hernandez, Danny, Brown, Tom, Conerly, Tom, DasSarma, Nova, Drain, Dawn, El-Showk, Sheer, Elhage, Nelson, Hatfield-Dodds, Zac, Henighan, Tom, Hume, Tristan, Johnston, Scott, Mann, Ben, Olah, Chris, Olsson, Catherine, Amodei, Dario, Joseph, Nicholas, Kaplan, Jared, McCandlish, Sam

“…Recent large language models have been trained on vast datasets, but also often on repeated data, either intentionally for the purpose of upweighting higher…”
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Influence of local symmetry on lattice dynamics coupled to topological surface states by Sobota, Jonathan A, Teitelbaum, Samuel W, Huang, Yijing, Querales-Flores, José D, Power, Robert, Allen, Meabh, Rotundu, Costel R, Bailey, Trevor P, Uher, Ctirad, Henighan, Tom, Jiang, Mason, Zhu, Diling, Chollet, Matthieu, Sato, Takahiro, Trigo, Mariano, Murray, Éamonn D, Savić, Ivana, Kirchmann, Patrick S, Fahy, Stephen, Reis, David. A, Shen, Zhi-Xun

“…We investigate coupled electron-lattice dynamics in the topological insulator Bi2Te3 with time-resolved photoemission and time-resolved x-ray diffraction. It…”
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In-context Learning and Induction Heads by Olsson, Catherine, Elhage, Nelson, Nanda, Neel, Joseph, Nicholas, DasSarma, Nova, Henighan, Tom, Mann, Ben, Askell, Amanda, Bai, Yuntao, Chen, Anna, Conerly, Tom, Drain, Dawn, Ganguli, Deep, Hatfield-Dodds, Zac, Hernandez, Danny, Johnston, Scott, Jones, Andy, Kernion, Jackson, Lovitt, Liane, Ndousse, Kamal, Amodei, Dario, Brown, Tom, Clark, Jack, Kaplan, Jared, McCandlish, Sam, Olah, Chris

“…"Induction heads" are attention heads that implement a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. In this work, we present…”
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Predictability and Surprise in Large Generative Models by Ganguli, Deep, Hernandez, Danny, Lovitt, Liane, DasSarma, Nova, Henighan, Tom, Jones, Andy, Joseph, Nicholas, Kernion, Jackson, Mann, Ben, Askell, Amanda, Bai, Yuntao, Chen, Anna, Conerly, Tom, Drain, Dawn, Elhage, Nelson, Showk, Sheer El, Fort, Stanislav, Hatfield-Dodds, Zac, Johnston, Scott, Kravec, Shauna, Nanda, Neel, Ndousse, Kamal, Olsson, Catherine, Amodei, Daniela, Amodei, Dario, Brown, Tom, Kaplan, Jared, McCandlish, Sam, Olah, Chris, Clark, Jack

“…Large-scale pre-training has recently emerged as a technique for creating capable, general purpose, generative models such as GPT-3, Megatron-Turing NLG,…”
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Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback by Bai, Yuntao, Jones, Andy, Ndousse, Kamal, Askell, Amanda, Chen, Anna, DasSarma, Nova, Drain, Dawn, Fort, Stanislav, Ganguli, Deep, Henighan, Tom, Joseph, Nicholas, Kadavath, Saurav, Kernion, Jackson, Conerly, Tom, El-Showk, Sheer, Elhage, Nelson, Hatfield-Dodds, Zac, Hernandez, Danny, Hume, Tristan, Johnston, Scott, Kravec, Shauna, Lovitt, Liane, Nanda, Neel, Olsson, Catherine, Amodei, Dario, Brown, Tom, Clark, Jack, McCandlish, Sam, Olah, Chris, Mann, Ben, Kaplan, Jared

“…We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We…”
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The Capacity for Moral Self-Correction in Large Language Models by Ganguli, Deep, Askell, Amanda, Schiefer, Nicholas, Liao, Thomas I, Lukošiūtė, Kamilė, Chen, Anna, Goldie, Anna, Mirhoseini, Azalia, Olsson, Catherine, Hernandez, Danny, Drain, Dawn, Li, Dustin, Tran-Johnson, Eli, Perez, Ethan, Kernion, Jackson, Kerr, Jamie, Mueller, Jared, Landau, Joshua, Ndousse, Kamal, Nguyen, Karina, Lovitt, Liane, Sellitto, Michael, Elhage, Nelson, Mercado, Noemi, DasSarma, Nova, Rausch, Oliver, Lasenby, Robert, Larson, Robin, Ringer, Sam, Kundu, Sandipan, Kadavath, Saurav, Johnston, Scott, Kravec, Shauna, Showk, Sheer El, Lanham, Tamera, Telleen-Lawton, Timothy, Henighan, Tom, Hume, Tristan, Bai, Yuntao, Hatfield-Dodds, Zac, Mann, Ben, Amodei, Dario, Joseph, Nicholas, McCandlish, Sam, Brown, Tom, Olah, Christopher, Clark, Jack, Bowman, Samuel R, Kaplan, Jared

“…We test the hypothesis that language models trained with reinforcement learning from human feedback (RLHF) have the capability to "morally self-correct" -- to…”
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Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned by Ganguli, Deep, Lovitt, Liane, Kernion, Jackson, Askell, Amanda, Bai, Yuntao, Kadavath, Saurav, Mann, Ben, Perez, Ethan, Schiefer, Nicholas, Ndousse, Kamal, Jones, Andy, Bowman, Sam, Chen, Anna, Conerly, Tom, DasSarma, Nova, Drain, Dawn, Elhage, Nelson, El-Showk, Sheer, Fort, Stanislav, Hatfield-Dodds, Zac, Henighan, Tom, Hernandez, Danny, Hume, Tristan, Jacobson, Josh, Johnston, Scott, Kravec, Shauna, Olsson, Catherine, Ringer, Sam, Tran-Johnson, Eli, Amodei, Dario, Brown, Tom, Joseph, Nicholas, McCandlish, Sam, Olah, Chris, Kaplan, Jared, Clark, Jack

“…We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful…”
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Language Models (Mostly) Know What They Know by Kadavath, Saurav, Conerly, Tom, Askell, Amanda, Henighan, Tom, Drain, Dawn, Perez, Ethan, Schiefer, Nicholas, Hatfield-Dodds, Zac, DasSarma, Nova, Tran-Johnson, Eli, Johnston, Scott, El-Showk, Sheer, Jones, Andy, Elhage, Nelson, Hume, Tristan, Chen, Anna, Bai, Yuntao, Bowman, Sam, Fort, Stanislav, Ganguli, Deep, Hernandez, Danny, Jacobson, Josh, Kernion, Jackson, Kravec, Shauna, Lovitt, Liane, Ndousse, Kamal, Olsson, Catherine, Ringer, Sam, Amodei, Dario, Brown, Tom, Clark, Jack, Joseph, Nicholas, Mann, Ben, McCandlish, Sam, Olah, Chris, Kaplan, Jared

“…We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We first show…”
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Constitutional AI: Harmlessness from AI Feedback by Bai, Yuntao, Kadavath, Saurav, Kundu, Sandipan, Askell, Amanda, Kernion, Jackson, Jones, Andy, Chen, Anna, Goldie, Anna, Mirhoseini, Azalia, McKinnon, Cameron, Chen, Carol, Olsson, Catherine, Olah, Christopher, Hernandez, Danny, Drain, Dawn, Ganguli, Deep, Li, Dustin, Tran-Johnson, Eli, Perez, Ethan, Kerr, Jamie, Mueller, Jared, Ladish, Jeffrey, Landau, Joshua, Ndousse, Kamal, Lukosuite, Kamile, Lovitt, Liane, Sellitto, Michael, Elhage, Nelson, Schiefer, Nicholas, Mercado, Noemi, DasSarma, Nova, Lasenby, Robert, Larson, Robin, Ringer, Sam, Johnston, Scott, Kravec, Shauna, Showk, Sheer El, Fort, Stanislav, Lanham, Tamera, Telleen-Lawton, Timothy, Conerly, Tom, Henighan, Tom, Hume, Tristan, Bowman, Samuel R, Hatfield-Dodds, Zac, Mann, Ben, Amodei, Dario, Joseph, Nicholas, McCandlish, Sam, Brown, Tom, Kaplan, Jared

“…As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant…”
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