Datasets and models for ACL 2026 paper: Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems.
AI & ML interests
Natural Language Processing at Yale
Recent Activity
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Papers
IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications
ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation
Dataset and models for paper: ANCHOR: Branch-Point Data Generation for GUI Agents
Model and data collection for our work "Understanding Reference Policies in Direct Preference Optimization" (https://arxiv.org/abs/2407.13709)
StepWise
Artifacts for paper: Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning (https://arxiv.org/abs/2508.19202)
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Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
Paper • 2508.19202 • Published • 7 -
yale-nlp/Qwen3-8B-SciLit01
Text Generation • 8B • Updated • 45 • 1 -
yale-nlp/qwen-instruct-synthetic_1_stem_only
Text Generation • 8B • Updated • 16 -
yale-nlp/qwen-instruct-synthetic_1_math_only
Text Generation • 8B • Updated • 18
Models and datasets for our work "MDCure: A Scalable Pipeline for Multi-Document Instruction-Following" (https://arxiv.org/abs/2410.23463)
Datasets and models for ACL 2026 paper: Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems.
StepWise
Dataset and models for paper: ANCHOR: Branch-Point Data Generation for GUI Agents
Artifacts for paper: Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning (https://arxiv.org/abs/2508.19202)
-
Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
Paper • 2508.19202 • Published • 7 -
yale-nlp/Qwen3-8B-SciLit01
Text Generation • 8B • Updated • 45 • 1 -
yale-nlp/qwen-instruct-synthetic_1_stem_only
Text Generation • 8B • Updated • 16 -
yale-nlp/qwen-instruct-synthetic_1_math_only
Text Generation • 8B • Updated • 18
Models and datasets for our work "MDCure: A Scalable Pipeline for Multi-Document Instruction-Following" (https://arxiv.org/abs/2410.23463)
Model and data collection for our work "Understanding Reference Policies in Direct Preference Optimization" (https://arxiv.org/abs/2407.13709)