3 citations · 9 across the 15 of their papers we have counts for
15 papers
Faster but Different: Diagnosing and Controlling Content Drift in Accelerated Multimodal Diffusion Language Models
Yaoxuan Dou, Yang Shu
Training-free acceleration makes diffusion-based multimodal large language models (dMLLMs) more deployable, but it may silently change generated content. We study this serving-time…
Localizing Prompt Ambiguity in Large Language Models with Probe-Targeted Attribution
Govind Ramesh, Yao Dou, Wei Xu
Prompt ambiguity is a common source of failure in large language models, but is difficult to localize because it is a latent property of the prompt, while existing attribution meth…
Gavel: Agent Meets Checklist for Evaluating LLMs on Long-Context Legal Summarization
Yao Dou, Benjamin Mamut, Wei Xu
Large language models (LLMs) now support contexts of up to 1M tokens, but their strengths and weaknesses on complex long-context tasks remain unclear. To study this, we focus on mu…
Evaluating LLMs on Chinese Idiom Translation
Cai Yang, Yao Dou, David Heineman +2
Idioms, whose figurative meanings usually differ from their literal interpretations, are common in everyday language, especially in Chinese, where they often contain historical ref…
Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI
Isadora Krsek, Anubha Kabra, Yao Dou +5
In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy…
Improving Minimum Bayes Risk Decoding with Multi-Prompt
David Heineman, Yao Dou, Wei Xu
While instruction fine-tuned LLMs are effective text generators, sensitivity to prompt construction makes performance unstable and sub-optimal in practice. Relying on a single "bes…
GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation
Govind Ramesh, Yao Dou, Wei Xu
Research on jailbreaking has been valuable for testing and understanding the safety and security issues of large language models (LLMs). In this paper, we introduce Iterative Refin…
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Yao Dou, Isadora Krsek, Tarek Naous +4
Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks. In this paper, we take the initiative to protect the user-side privacy asso…
Automatic and Human-AI Interactive Text Generation
Yao Dou, Philippe Laban, Claire Gardent +1
In this tutorial, we focus on text-to-text generation, a class of natural language generation (NLG) tasks, that takes a piece of text as input and then generates a revision that is…
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation
David Heineman, Yao Dou, Wei Xu
Fine-grained, span-level human evaluation has emerged as a reliable and robust method for evaluating text generation tasks such as summarization, simplification, machine translatio…
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA
David Heineman, Yao Dou, Mounica Maddela +1
Large language models (e.g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding o…
LENS: A Learnable Evaluation Metric for Text Simplification
Mounica Maddela, Yao Dou, David Heineman +1
Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation. However, existing human eval…
Improving Large-scale Paraphrase Acquisition and Generation
Yao Dou, Chao Jiang, Wei Xu
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identificatio…
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski +2
Modern neural language models can produce remarkably fluent and grammatical text. So much, in fact, that recent work by Clark et al. (2021) has reported that conventional crowdsour…
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
Yao Dou, Maxwell Forbes, Ari Holtzman +1
We study conversational dialog in which there are many possible responses to a given history. We present the MultiTalk Dataset, a corpus of over 320,000 sentences of written conver…