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20212023
most citedIs ChatGPT a General-Purpose Natural Language Processing Task Solver?

62 citations · 430 across the 52 of their papers we have counts for

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Showing 2023Show all

17 papers · 1 filter

cs.AI2023★ 55 cited

Identifying and Mitigating the Security Risks of Generative AI

Clark Barrett, Brad Boyd, Elie Burzstein +20

Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, s…

cs.HC2023

Shaping the Emerging Norms of Using Large Language Models in Social Computing Research

Hong Shen, Tianshi Li, Toby Jia-Jun Li +2

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analy…

cs.CL2023★ 1 cited

Modeling Cross-Cultural Pragmatic Inference with Codenames Duet

Omar Shaikh, Caleb Ziems, William Held +3

Pragmatic reference enables efficient interpersonal communication. Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers an…

cs.CV2023★ 27 cited

LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu +4

Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visu…

cs.CL2023★ 6 cited

TADA: Task-Agnostic Dialect Adapters for English

Will Held, Caleb Ziems, Diyi Yang

Large Language Models, the dominant starting point for Natural Language Processing (NLP) applications, fail at a higher rate for speakers of English dialects other than Standard Am…

cs.CL2023

Forgotten Knowledge: Examining the Citational Amnesia in NLP

Janvijay Singh, Mukund Rungta, Diyi Yang +1

Citing papers is the primary method through which modern scientific writing discusses and builds on past work. Collectively, citing a diverse set of papers (in time and area of stu…

cs.CL2023

NormBank: A Knowledge Bank of Situational Social Norms

Caleb Ziems, Jane Dwivedi-Yu, Yi-Chia Wang +2

We present NormBank, a knowledge bank of 155k situational norms. This resource is designed to ground flexible normative reasoning for interactive, assistive, and collaborative AI s…

cs.CL2023★ 20 cited

Benchmarking Machine Translation with Cultural Awareness

Binwei Yao, Ming Jiang, Tara Bobinac +2

Translating culture-related content is vital for effective cross-cultural communication. However, many culture-specific items (CSIs) often lack viable translations across languages…

cs.CL2023

DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

Yanchen Liu, William Held, Diyi Yang

Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialect…

cs.CL2023★ 62 cited

Can Large Language Models Transform Computational Social Science?

Caleb Ziems, William Held, Omar Shaikh +3

Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify a…

cs.CL2023★ 4 cited

Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP

Anya Belz, Craig Thomson, Ehud Reiter +39

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/le…

cs.CL2023★ 1 cited

A Cheaper and Better Diffusion Language Model with Soft-Masked Noise

Jiaao Chen, Aston Zhang, Mu Li +2

Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation. Whereas, as a way inherently bui…

cs.CL2023★ 2 cited

Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints

Albert Lu, Hongxin Zhang, Yanzhe Zhang +2

The limits of open-ended generative models are unclear, yet increasingly important. What causes them to succeed and what causes them to fail? In this paper, we take a prompt-centri…

cs.CV2023★ 9 cited

Auditing Gender Presentation Differences in Text-to-Image Models

Yanzhe Zhang, Lu Jiang, Greg Turk +1

Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools. Despite significantly affecting a wide ra…

cs.CL2023★ 62 cited

Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Chengwei Qin, Aston Zhang, Zhuosheng Zhang +3

Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., with…

cs.CL2023★ 17 cited

Parameter-Efficient Fine-Tuning Design Spaces

Jiaao Chen, Aston Zhang, Xingjian Shi +3

Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit,…

cs.SI2023★ 8 cited

Just Another Day on Twitter: A Complete 24 Hours of Twitter Data

Juergen Pfeffer, Daniel Matter, Kokil Jaidka +13

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of int…