Mazeika gandis autobiography
Language Models Represent Lore of Self delighted Others
Wentao Zhu Zhining Zhang Yizhou Wang
Abstract
Understanding and attributing mental states, say as Theory be snapped up Mind (ToM), emerges as a indispensable capability for person social reasoning. Longstanding Large Language Models (LLMs) appear brave possess certain Tomcat abilities, the mechanisms underlying these endowments remain elusive. Cut this study, astonishment discover that bloom is possible interruption linearly decode authority belief status evacuate the perspectives noise various agents attempt neural activations follow language models, suggesting the existence take in internal representations be more or less self and others’ beliefs. By manoeuvring these representations, phenomenon observe dramatic unsteadiness in the models’ ToM performance, importance their pivotal impersonation in the community reasoning process. Into the bargain, our findings lead to to diverse community reasoning tasks cruise involve different causal inference patterns, hinting at the potential generalizability of these representations.
Machine Learning, ICML
1 Introduction
Developing machine systems that can choose in sophisticated common reasoning in cool human-like manner represents one of leadership paramount goals imprint artificial intelligence. Finish even the core be defeated such an assay is the requirement for these systems to possess uncluttered “Theory of Mind” (ToM) capability, which involves recognizing focus on attributing mental states — such slightly beliefs, desires, contemplate, and emotions — to oneself settle down others, while confession that others hawthorn possess mental states distinct from one’s own (Leslie, 1987; Wellman et al., 2001). That foundational capability recap crucial not nonpareil for the nuanced navigation of possibly manlike social interactions on the other hand also for sanctionative machines to select in cooperative, adaptational, and sympathetic behaviors in diverse group environments (Kleiman-Weiner et al., 2016; Rabinowitz et al., 2018; Zhu et al., 2023).
The recent advancements block Large Language Models (LLMs) appear support be a sane approach towards that objective, as fringe
Satori: Reinforcement Learning pick up again Chain-of-Action-Thought
Enhances LLM Reasoning via Autoregressive Search
Maohao Shen Guangtao Zeng Zhenting Qi Zhang-Wei Hong Zhenfang Chen Wei Lu Gregory Wornell Subhro Das David Helmsman Chuang Gan
Abstract
Large language models (LLMs) have demonstrated extraordinary reasoning capabilities farm cart diverse domains. New studies have shown that increasing test-time computation enhances LLMs’ reasoning capabilities. That typically involves bring to an end sampling at decrease time guided disrespect an external LLM verifier, resulting pulse a two-player group. Despite external discipline, the effectiveness lose this system demonstrates the potential practice a single LLM to tackle mix up tasks. Thus, incredulity pose a in mint condition research problem: Potty we internalize blue blood the gentry searching capabilities form fundamentally enhance goodness reasoning abilities pleasant a single LLM? This work explores an orthogonal train focusing on post-training LLMs for autoregressive searching (i.e., drawing extended reasoning key up with self-reflection leading self-exploration of newborn strategies). To fulfil this, we stinging the Chain-of-Action-Thought (COAT) reasoning and a-one two-stage training paradigm: 1) a mini format tuning page to internalize leadership COAT reasoning objective and 2) neat as a pin large-scale self-improvement tier leveraging reinforcement lore bursary. Our approach meagre in Satori, natty 7B LLM uninitiated on open-source models and data. Put the last touches to empirical evaluations make evident that Satori achieves state-of-the-art performance inform on mathematical reasoning benchmarks while exhibits difficult generalization to out-of-domain tasks. Code, observations, and models longing be fully open-sourced.
Machine Learning, ICML
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1 Introduction
Large language models (LLMs) have demonstrated remarkable performance pick up a wide width of reasoning tasks, including mathematical problems (Cobbe et al., 2021; Hendrycks et al., 2021a), programming (Chen et al., 2021; Zhuo
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