3 citations · 9 across the 18 of their papers we have counts for
11 papers · 1 filter
Towards a Universal Causal Reasoner
Qirun Dai, Xiao Liu, Jiawei Zhang +3
Despite the importance of causal reasoning, training LLMs to reason causally remains underexplored. Existing data efforts mostly focus on benchmarking LLMs on specific aspects of c…
Code as Agent Harness
Xuying Ning, Katherine Tieu, Dongqi Fu +39
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineerin…
Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarcity
Dylan Zhang, Justin Wang, Tianran Sun
Existing LMs struggle with proof-oriented programming due to data scarcity, which manifest in two key ways: (1) a lack of sufficient corpora for proof-oriented programming language…
The Best Instruction-Tuning Data are Those That Fit
Dylan Zhang, Qirun Dai, Hao Peng
High-quality supervised fine-tuning (SFT) data are crucial for eliciting strong capabilities from pretrained large language models (LLMs). Typically, instructions are paired with m…
Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
Qirun Dai, Dylan Zhang, Jiaqi W. Ma +1
Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced p…
:Revealing the Decisive Effect of Instruction Diversity on Generalization
Dylan Zhang, Justin Wang, Francois Charton
Understanding and accurately following instructions is critical for large language models (LLMs) to be effective across diverse tasks. In this work, we rigorously examine the key f…
: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
Dylan Zhang, Shizhe Diao, Xueyan Zou +1
Preference learning provides a promising solution to address the limitations of supervised fine-tuning (SFT) for code language models, where the model is not explicitly trained to…
From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers
Dylan Zhang, Justin Wang, Francois Charton
Instruction tuning -- tuning large language models on instruction-output pairs -- is a promising technique for making models better adapted to the real world. Yet, the key factors…
Instruction Diversity Drives Generalization To Unseen Tasks
Dylan Zhang, Justin Wang, Francois Charton
Instruction tuning -- fine-tuning a large language model (LLM) on pairs of instructions and desired outcomes -- is an approach that enables pre-trained language models to perform r…
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion
Dylan Zhang, Curt Tigges, Zory Zhang +3
This paper investigates the ability of transformer-based models to learn structural recursion from examples. Recursion is a universal concept in both natural and formal languages.…
PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis
Dylan Zhang, Xuchao Zhang, Chetan Bansal +3
Major cloud providers have employed advanced AI-based solutions like large language models to aid humans in identifying the root causes of cloud incidents. Despite the growing prev…