Search Results - "McCandlish, Sam"

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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Towards Understanding Sycophancy in Language Models by Sharma, Mrinank, Tong, Meg, Korbak, Tomasz, Duvenaud, David, Askell, Amanda, Bowman, Samuel R, Cheng, Newton, Durmus, Esin, Hatfield-Dodds, Zac, Johnston, Scott R, Kravec, Shauna, Maxwell, Timothy, McCandlish, Sam, Ndousse, Kamal, Rausch, Oliver, Schiefer, Nicholas, Yan, Da, Zhang, Miranda, Perez, Ethan

“…Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful…”
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Studying Large Language Model Generalization with Influence Functions by Grosse, Roger, Bae, Juhan, Anil, Cem, Elhage, Nelson, Tamkin, Alex, Tajdini, Amirhossein, Steiner, Benoit, Li, Dustin, Durmus, Esin, Perez, Ethan, Hubinger, Evan, Lukošiūtė, Kamilė, Nguyen, Karina, Joseph, Nicholas, McCandlish, Sam, Kaplan, Jared, Bowman, Samuel R

“…When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of…”
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Towards Measuring the Representation of Subjective Global Opinions in Language Models by Durmus, Esin, Nguyen, Karina, Liao, Thomas I, Schiefer, Nicholas, Askell, Amanda, Bakhtin, Anton, Chen, Carol, Hatfield-Dodds, Zac, Hernandez, Danny, Joseph, Nicholas, Lovitt, Liane, McCandlish, Sam, Sikder, Orowa, Tamkin, Alex, Thamkul, Janel, Kaplan, Jared, Clark, Jack, Ganguli, Deep

“…Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to…”
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An Empirical Model of Large-Batch Training by McCandlish, Sam, Kaplan, Jared, Amodei, Dario, Team, OpenAI Dota

“…In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing…”
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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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Measuring Faithfulness in Chain-of-Thought Reasoning by Lanham, Tamera, Chen, Anna, Radhakrishnan, Ansh, Steiner, Benoit, Denison, Carson, Hernandez, Danny, Li, Dustin, Durmus, Esin, Hubinger, Evan, Kernion, Jackson, Lukošiūtė, Kamilė, Nguyen, Karina, Cheng, Newton, Joseph, Nicholas, Schiefer, Nicholas, Rausch, Oliver, Larson, Robin, McCandlish, Sam, Kundu, Sandipan, Kadavath, Saurav, Yang, Shannon, Henighan, Thomas, Maxwell, Timothy, Telleen-Lawton, Timothy, Hume, Tristan, Hatfield-Dodds, Zac, Kaplan, Jared, Brauner, Jan, Bowman, Samuel R, Perez, Ethan

“…Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear…”
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Question Decomposition Improves the Faithfulness of Model-Generated Reasoning by Radhakrishnan, Ansh, Nguyen, Karina, Chen, Anna, Chen, Carol, Denison, Carson, Hernandez, Danny, Durmus, Esin, Hubinger, Evan, Kernion, Jackson, Lukošiūtė, Kamilė, Cheng, Newton, Joseph, Nicholas, Schiefer, Nicholas, Rausch, Oliver, McCandlish, Sam, Showk, Sheer El, Lanham, Tamera, Maxwell, Tim, Chandrasekaran, Venkatesa, Hatfield-Dodds, Zac, Kaplan, Jared, Brauner, Jan, Bowman, Samuel R, Perez, Ethan

“…As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help…”
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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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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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A General Language Assistant as a Laboratory for Alignment by Askell, Amanda, Bai, Yuntao, Chen, Anna, Drain, Dawn, Ganguli, Deep, Henighan, Tom, Jones, Andy, Joseph, Nicholas, Mann, Ben, DasSarma, Nova, Elhage, Nelson, Hatfield-Dodds, Zac, Hernandez, Danny, Kernion, Jackson, Ndousse, Kamal, Olsson, Catherine, Amodei, Dario, Brown, Tom, Clark, Jack, McCandlish, Sam, Olah, Chris, Kaplan, Jared

“…Given the broad capabilities of large language models, it should be possible to work towards a general-purpose, text-based assistant that is aligned with human…”
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Measuring Progress on Scalable Oversight for Large Language Models by Bowman, Samuel R, Hyun, Jeeyoon, Perez, Ethan, Chen, Edwin, Pettit, Craig, Heiner, Scott, Lukošiūtė, Kamilė, Askell, Amanda, Jones, Andy, Chen, Anna, Goldie, Anna, Mirhoseini, Azalia, McKinnon, Cameron, Olah, Christopher, Amodei, Daniela, Amodei, Dario, Drain, Dawn, Li, Dustin, Tran-Johnson, Eli, Kernion, Jackson, Kerr, Jamie, Mueller, Jared, Ladish, Jeffrey, Landau, Joshua, Ndousse, Kamal, Lovitt, Liane, Elhage, Nelson, Schiefer, Nicholas, Joseph, Nicholas, Mercado, Noemí, DasSarma, Nova, Larson, Robin, McCandlish, Sam, Kundu, Sandipan, Johnston, Scott, Kravec, Shauna, Showk, Sheer El, Fort, Stanislav, Telleen-Lawton, Timothy, Brown, Tom, Henighan, Tom, Hume, Tristan, Bai, Yuntao, Hatfield-Dodds, Zac, Mann, Ben, Kaplan, Jared

“…Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that…”
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Scaling Laws for Neural Language Models by Kaplan, Jared, McCandlish, Sam, Henighan, Tom, Brown, Tom B, Chess, Benjamin, Child, Rewon, Gray, Scott, Radford, Alec, Wu, Jeffrey, Amodei, Dario

“…We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the…”
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