4 citations · 9 across the 26 of their papers we have counts for
17 papers · 1 filter
IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation
Jiarui Liu, Renjie Tao, Yiwei Liao +15
Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge. We isolate research ideation as a s…
Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning
Yinghui He, Ling Yang, Jiarui Liu +6
Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result t…
MixSD: Mixed Contextual Self-Distillation for Knowledge Injection
Jiarui Liu, Lechen Zhang, Yongjin Yang +5
Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasoning and general-domain perform…
PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf
Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner +17
Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because revi…
Re-Centering Humans in LLM Personalization
Lechen Zhang, Jiarui Liu, Tal August
Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains unclear how well current personaliz…
OdysSim: Building Foundation Models for Human Behavior Simulation
Xuhui Zhou, Weiwei Sun, Weihua Du +6
Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven post-training pulls them toward a homog…
Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision
Yinghui He, Simran Kaur, Adithya Bhaskar +7
Current post-training methods in verifiable settings fall into two categories. Reinforcement learning (RLVR) relies on binary rewards, which are broadly applicable and powerful, bu…
Are LLMs Good Safety Agents or a Propaganda Engine?
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a reflection of its safety policies…
CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures
Punya Syon Pandey, Yongjin Yang, Jiarui Liu +1
Game-theoretic interactions between agents with Large Language Models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not be…
Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing
Neemesh Yadav, Jiarui Liu, Francesco Ortu +3
The ability of Natural Language Processing (NLP) methods to categorize text into multiple classes has motivated their use in online content moderation tasks, such as hate speech an…
Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
Jiarui Liu, Iman Ouzzani, Wenkai Li +5
The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduce GIST, a l…
BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data
Wenkai Li, Jiarui Liu, Andy Liu +3
In this work, we tackle the challenge of embedding realistic human personality traits into LLMs. Previous approaches have primarily focused on prompt-based methods that describe th…
Language Model Alignment in Multilingual Trolley Problems
Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti +9
We evaluate the moral alignment of LLMs with human preferences in multilingual trolley problems. Building on the Moral Machine experiment, which captures over 40 million human judg…
Automatic Generation of Model and Data Cards: A Step Towards Responsible AI
Jiarui Liu, Wenkai Li, Zhijing Jin +1
In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardi…
Analyzing the Role of Semantic Representations in the Era of Large Language Models
Zhijing Jin, Yuen Chen, Fernando Gonzalez +5
Traditionally, natural language processing (NLP) models often use a rich set of features created by linguistic expertise, such as semantic representations. However, in the era of l…
Implicit Personalization in Language Models: A Systematic Study
Zhijing Jin, Nils Heil, Jiarui Liu +5
Implicit Personalization (IP) is a phenomenon of language models inferring a user's background from the implicit cues in the input prompts and tailoring the response based on this…
Interactive Molecular Discovery with Natural Language
Zheni Zeng, Bangchen Yin, Shipeng Wang +7
Natural language is expected to be a key medium for various human-machine interactions in the era of large language models. When it comes to the biochemistry field, a series of tas…