48 citations · 60 across the 12 of their papers we have counts for
12 papers
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
Fan Yin, Jayanth Srinivasa, Kai-Wei Chang
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs), which serves as a crucial step in building trust between humans and…
Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks
Po-Nien Kung, Fan Yin, Di Wu +2
Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models (LLMs) on a massive amount of diverse tasks with instructions. Howeve…
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Hammaad Adam, Fan Yin, Huibin +5
Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment ear…
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning
Fan Yin, Jesse Vig, Philippe Laban +3
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks. However, it remains unclear whether models truly un…
Efficient Shapley Values Estimation by Amortization for Text Classification
Chenghao Yang, Fan Yin, He He +3
Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model e…
Red Teaming Language Model Detectors with Language Models
Zhouxing Shi, Yihan Wang, Fan Yin +3
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially decept…
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Da Yin, Xiao Liu, Fan Yin +4
Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either ma…
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Hritik Bansal, Nishad Singhi, Yu Yang +3
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data. However, previous studies have…
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Fan Yin, Yao Li, Cho-Jui Hsieh +1
Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) commun…
On the Sensitivity and Stability of Model Interpretations in NLP
Fan Yin, Zhouxing Shi, Cho-Jui Hsieh +1
Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions. Despite the su…
On the Robustness of Language Encoders against Grammatical Errors
Fan Yin, Quanyu Long, Tao Meng +1
We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we c…
Entity-Relation Extraction as Multi-Turn Question Answering
Xiaoya Li, Fan Yin, Zijun Sun +6
In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities a…