2 citations · 5 across the 11 of their papers we have counts for
12 papers
On the Behavioral Traits of LLM Agents
Haokai Zhao, Jie Gao, Yunze Xiao +5
Users increasingly describe different AI agents as distinct colleagues to work with. AI personality research aims to quantify such impressions by attributing human- like "traits" t…
Group Alignment-Induced Sycophancy: A Two-Sided Evaluation of Steerable Pluralistic Alignment
Haokai Zhao, Yunze Xiao, Weihao Xuan +3
Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences. Sycophancy, a well-documented by-pro…
CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning
Rahul Markasserithodi, Aditya Joshi, Yuekang Li +3
Despite advances in safety alignment, prompt-rewriting attacks such as persona modulation, fictional framing and persuasion-based reformulation, can bypass safety filters even on f…
A Benchmark Construction and Evaluation Framework for Specialist Domains: Case Study on Defense-related Documents
Bao Gia Doan, Aditya Joshi, Pantelis Elinas +4
RAG-based question-answering (QA) in specialist domains faces a cold-start problem: lack of evaluative benchmarks and absence of labeled data for post-training. We present DoRA (Do…
TeamUp: Semantic Project Matching and Team Formation for Learning at Scale
Dhruv Gulwani, Basem Suleiman, Aditya Joshi +1
Project-based learning improves student engagement and learning outcomes, yet allocating students to appropriately challenging projects while forming cognitively diverse teams rema…
NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
Fang Wu, Haokai Zhao, Da Xing +18
Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively throu…
Alternatives To Next Token Prediction In Text Generation -- A Survey
Charlie Wyatt, Aditya Joshi, Flora Salim
The paradigm of Next Token Prediction (NTP) has driven the unprecedented success of Large Language Models (LLMs), but is also the source of their most persistent weaknesses such as…
What am I missing here?: Evaluating Large Language Models for Masked Sentence Prediction
Charlie Wyatt, Aditya Joshi, Flora Salim
Transformer-based models primarily rely on Next Token Prediction (NTP), which predicts the next token in a sequence based on the preceding context. However, NTP's focus on single-t…
Harnessing Test-time Adaptation for NLU tasks Involving Dialects of English
Duke Nguyen, Aditya Joshi, Flora Salim
Test-time domain adaptation (TTDA) is an excellent method which helps generalize models across domains, tasks, and distributions without the use of labeled datasets. Thus, TTDA is…
"Is Hate Lost in Translation?": Evaluation of Multilingual LGBTQIA+ Hate Speech Detection
Fai Leui Chan, Duke Nguyen, Aditya Joshi
This paper explores the challenges of detecting LGBTQIA+ hate speech of large language models across multiple languages, including English, Italian, Chinese and (code-switched) Eng…
Spectraformer: A Unified Random Feature Framework for Transformer
Duke Nguyen, Du Yin, Aditya Joshi +1
Linearization of attention using various kernel approximation and kernel learning techniques has shown promise. Past methods used a subset of combinations of component functions an…
Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection
Duke Nguyen, Khaing Myat Noe Naing, Aditya Joshi
This paper reports our submission under the team name `SynthDetectives' to the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the task of AI-generated text d…