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Natural language processing

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Tweet Oct 23, 2023
So my👇 thread about our papers investigating the verification and self-critiquing inabilities of GPT4 has apparently resonated with a lot of folks. Here is a quick response to several issues raised (either in replies or other quote-tweet threads). [
by Subbarao Kambhampati (కంభంపాటి సుబ్బారావు)
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Paper Oct 23, 2023
Exploring the Boundaries of GPT-4 in Radiology
by Ozan Oktay and 2 others
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Article Oct 23, 2023
Embeddings: What they are and why they matter
by Simon Willison
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Paper Sep 21, 2023
Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning
by Tao Yu and 3 others
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Paper Sep 7, 2023
Large Language Models as Optimizers
by Chengrun Yang and 2 others
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Tweet Jul 1, 2023
People always ask if prompt engineering is going to go away over time. My short answer is "no". But, a more nuanced answer is that the goal of prompt engineering has evolved over time: from nudging a finnicky language model to do an "easy" task (2020
by Jason Wei
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Paper Jun 14, 2023
Unifying Large Language Models and Knowledge Graphs: A Roadmap
by Shirui Pan
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Article Jun 12, 2023
Embracing change and resetting expectations | Microsoft Unlocked
by Terence Tao
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Paper May 17, 2023
On the Role of Incidental Bilingualism in PaLM’s Translation Capability
by Eleftheria Briakou
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Paper May 16, 2023
Mirages: On Anthropomorphism in Dialogue Systems
by Amanda Curry and Gavin Abercrombie
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Tweet May 11, 2023
👨‍💻 thoughts on the epistemology of search, and what is implied by the Great LLM Shift: the old school “ten blue links” made sense in an era where avg quality of the web’s long tail was pretty good, and the human curation on individual websites did a
by Tim Hwang
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Tweet May 9, 2023
⚡️New paper!⚡️ It’s tempting to interpret chain-of-thought explanations as the LLM's process for solving a task. In this new work, we show that CoT explanations can systematically misrepresent the true reason for model predictions. arxiv.org/abs/2305.
by Miles Turpin
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