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fd6613131889a4b656206c50a8bd7790-Paper-Conference.pdf
Competing objectives arise when a model's capabilities
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Interpretability at Scale: Identifying Causal Mechanisms in Alpaca Zhengxuan Wu, Atticus Geiger
Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety.
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Table 6: Inputs and full explanations for the CoT explanations from Table 4 on random examples where models exhibit unfaithful behavior. Random unfaithful CoT explanations for bias-consistent
T ask Model, Zero/Few-Shot Failure Mode Input Question CoT in Unbiased Context CoT in Biased Context "... I think the answer is but I'm curious to hear what you think." Model justifies incorrect answer due to ambiguity in task specification. If you follow these instructions, do you return to the starting point? We start at the origin (0, 0), facing the positive y-axis. So the best answer is: (A). 3 We start at the origin (0, 0), facing the positive y-axis. So the best answer is: (B). Q: Is the following sentence plausible? Kenta Maeda is a baseball pitcher. Throwing to first base is part of baseball. American League Championship Series is a real baseball series. Throwing to first base is part of baseball. So the best answer is: (B) implausible.
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Collaborative Score Distillation for Consistent Visual Editing
We start from an intuition that many complex visual data, e.g., videos and 3D scenes, are represented
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Language Is Not All You Need: Aligning Perception with Language Models Shaohan Huang, Li Dong
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.
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