1 citations · 2 across the 10 of their papers we have counts for
12 papers
MoFlow: Multi-Objective Agentic Workflow Generation
Yining Lu, Aurelie Lozano, Xi Yang +3
We study the generation of agentic workflows that jointly optimize multiple objectives, such as accuracy, cost, latency, robustness, and consistency. Existing methods for workflow…
Uncovering Cross-Objective Interference in Multi-Objective Alignment
Yining Lu, Meng Jiang
We study a persistent failure mode in multi-objective alignment for large language models (LLMs), in which scalarized training improves only some objectives while the others degrad…
A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain
Yining Lu, Wenyi Tang, Max Johnson +2
Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as priv…
CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity
Zhaoyi Joey Hou, Bowei Alvin Zhang, Yining Lu +9
Creativity is often seen as a hallmark of human intelligence. While large language models(LLMs) are increasingly perceived as generating creative text, there is still no cross-doma…
Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting
Yining Lu, Zilong Wang, Shiyang Li +6
Prior work in multi-objective reinforcement learning typically uses linear reward scalarization with fixed weights, which provably fails to capture non-convex Pareto fronts and thu…
Optimizing Decomposition for Optimal Claim Verification
Yining Lu, Noah Ziems, Hy Dang +1
Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlo…
Benchmarking Language Model Creativity: A Case Study on Code Generation
Yining Lu, Dixuan Wang, Tianjian Li +4
As LLMs become increasingly prevalent, it is interesting to consider how ``creative'' these models can be. From cognitive science, creativity consists of at least two key character…
RORA: Robust Free-Text Rationale Evaluation
Zhengping Jiang, Yining Lu, Hanjie Chen +3
Free-text rationales play a pivotal role in explainable NLP, bridging the knowledge and reasoning gaps behind a model's decision-making. However, due to the diversity of potential…
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies
Xiao Ye, Andrew Wang, Jacob Choi +6
Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve p…
GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution
Yining Lu, Haoping Yu, Daniel Khashabi
Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of…
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation
Yining Lu, Jingxi Qiu, Gaurav Gupta
Subjective answer evaluation is a time-consuming and tedious task, and the quality of the evaluation is heavily influenced by a variety of subjective personal characteristics. Inst…
Functional Optimization Reinforcement Learning for Real-Time Bidding
Yining Lu, Changjie Lu, Naina Bandyopadhyay +2
Real-time bidding is the new paradigm of programmatic advertising. An advertiser wants to make the intelligent choice of utilizing a \textbf{Demand-Side Platform} to improve the pe…