1 citations · 3 across the 9 of their papers we have counts for
9 papers · 1 filter
Do Language Models Track Entities Across State Changes?
Zilu Tang, Qiao Zhao, Gabriel Franco +4
Entity tracking (ET), the ability to keep track of states, is a fundamental skill that underlies complex reasoning. An increasing amount of work investigates how transformer langua…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
mR3: Multilingual Rubric-Agnostic Reward Reasoning Models
David Anugraha, Shou-Yi Hung, Zilu Tang +3
Evaluation using Large Language Model (LLM) judges has been widely adopted in English and shown to be effective for automatic evaluation. However, their performance does not genera…
R3: Robust Rubric-Agnostic Reward Models
David Anugraha, Zilu Tang, Lester James V. Miranda +5
Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models a…
Is Active Persona Inference Necessary for Aligning Small Models to Personal Preferences?
Zilu Tang, Afra Feyza Akyürek, Ekin Akyürek +1
A prominent issue in aligning language models (LMs) to personalized preferences is underspecification -- the lack of information from users about their preferences. A popular trend…
A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information
Lucky Susanto, Musa Wijanarko, Prasetia Pratama +6
Polarization is defined as divisive opinions held by two or more groups on substantive issues. As the world's third-largest democracy, Indonesia faces growing concerns about the in…
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
Zilu Tang, Rajen Chatterjee, Sarthak Garg
Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-dec…
Could We Have Had Better Multilingual LLMs If English Was Not the Central Language?
Ryandito Diandaru, Lucky Susanto, Zilu Tang +2
Large Language Models (LLMs) demonstrate strong machine translation capabilities on languages they are trained on. However, the impact of factors beyond training data size on trans…
Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations
Zilu Tang, Mayank Agarwal, Alex Shypula +4
This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanat…