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20162023
most citedSparks of Artificial General Intelligence: Early experiments with GPT-4

1.6k citations · 3k across the 19 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.HC2023★ 11 cited

AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms

Zana Buçinca, Chau Minh Pham, Maurice Jakesch +3

While demands for change and accountability for harmful AI consequences mount, foreseeing the downstream effects of deploying AI systems remains a challenging task. We developed AH…

cs.CL2023★ 2 cited

Targeted Data Generation: Finding and Fixing Model Weaknesses

Zexue He, Marco Tulio Ribeiro, Fereshte Khani

Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Addi…

cs.LG2023★ 1 cited

Collaborative Development of NLP models

Fereshte Khani, Marco Tulio Ribeiro

Despite substantial advancements, Natural Language Processing (NLP) models often require post-training adjustments to enforce business rules, rectify undesired behavior, and align…

cs.CL2023★ 1.6k cited

Sparks of Artificial General Intelligence: Early experiments with GPT-4

Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan +11

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks,…

cs.HC2023★ 73 cited

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King +2

Large language models are becoming increasingly pervasive and ubiquitous in society via deployment in sociotechnical systems. Yet these language models, be it for classification or…

cs.CL2023★ 50 cited

ART: Automatic multi-step reasoning and tool-use for large language models

Bhargavi Paranjape, Scott Lundberg, Sameer Singh +3

Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning…

cs.HC2023★ 23 cited

ScatterShot: Interactive In-context Example Curation for Text Transformation

Tongshuang Wu, Hua Shen, Daniel S. Weld +2

The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an LLM to their specific tasks with a small number of examples. However, users tend to include…