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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Status: Unchecked ×Keyword: in-context learning ×Clear all

7 claims from 5 papers

1. Computer Science › Topic Modeling

   A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development

   Wu, He, Liu et al. · IEEE/CAA Journal of Automatica Sinica · 2023

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   1. Unchecked“Specifically, from the limited open-accessed resources, we conclude the core techniques of ChatGPT, mainly including large-scale language models, in-context learning, reinforcement learning from human feedback and the key technical steps for developing Chat-…

2. Computer Science › Multimodal Machine Learning Applications

   VILA: On Pre-training for Visual Language Models

   Ji, Yin, Ping, Molchanov, Shoeybi and Han · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2024

   The paper studies how to pre-train visual language models, reports three findings on design choices, and uses them to build VILA, a model family that also shows extra abilities and runs on-device.

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   1. UncheckedThe authors say VILA, built with an improved pre-training recipe, consistently beats state-of-the-art models such as LLaVA-1.5 on main benchmarks.“With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, e.g., LLaVA-1.5, across main benchmarks without bells and whistles.”
   2. UncheckedThe paper says multi-modal pre-training brings out useful abilities in its VILA models: multi-image reasoning, stronger in-context learning and better world knowledge.“Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.”

3. Engineering › Traffic Prediction and Management Techniques

   Exploring the roles of large language models in reshaping transportation systems: A survey, framework, and roadmap

   Nie, Sun and Ma · Artificial Intelligence for Transportation · 2025

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   1. Unchecked“Extensive knowledge and high-level capabilities derived from pretraining evolve the default role of LLMs as text generators to become versatile, knowledge-driven task solvers for intelligent transportation systems.”

4. Social Sciences › Ethics and Social Impacts of AI

   AI and the End of an Era

   Meinke, Schoen, Scheurer, Balesni, Shah and Hobbhahn · arXiv (Cornell University) · 2024

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   1. Unchecked“Our results show that o1, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, and Llama 3.1 405B all demonstrate in-context scheming capabilities.”

5. Computer Science › Topic Modeling

   Text Classification via Large Language Models

   Sun, Li, Li et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance comparable to SOTA on MR (92.39 v.s. 93.3)…
   2. Unchecked“Specifically, using 16 examples per class, CARP achieves comparable performances to supervised models with 1,024 examples per class.”