62 citations · 430 across the 52 of their papers we have counts for
12 papers · 2 filters
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…