6 citations · 7 across the 13 of their papers we have counts for
13 papers
Signed Rescue Routing: Harm-Aware Cascades for Efficient LLM Inference
Zheyuan Wang, Siyu Li, Peiqiao Song +3
Large language model (LLM) cascades answer easy requests with a small model and escalate selected requests to a larger model. Most routers prioritize examples on which the small mo…
BaziQA-Benchmark: Evaluating Symbolic and Temporally Compositional Reasoning in Large Language Models
Jiangxi Chen, Qian Liu
We present BaziQA-Benchmark, a standardized benchmark for evaluating symbolic and temporally compositional reasoning in large language models. The benchmark is derived from 200 pro…
RC-GRPO: Reward-Conditioned Group Relative Policy Optimization for Multi-Turn Tool Calling Agents
Haitian Zhong, Jixiu Zhai, Lei Song +3
Multi-turn tool calling is challenging for Large Language Models (LLMs) because rewards are sparse and exploration is expensive. A common recipe, SFT followed by GRPO, can stall wh…
Multi-turn Training with Basic Human Feedback Helps Little on LLM Reasoning
Qiang Liu, Wuganjing Song, Zhenzhou Lin +4
The reasoning capabilities of Large Language Models (LLMs) are typically developed through the single-turn reinforcement learning, whereas real-world applications often involve mul…
UPRPRC: Unified Pipeline for Reproducing Parallel Resources -- Corpus from the United Nations
Qiuyang Lu, Fangjian Shen, Zhengkai Tang +4
The quality and accessibility of multilingual datasets are crucial for advancing machine translation. However, previous corpora built from United Nations documents have suffered fr…
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
Yaxin Du, Yuzhu Cai, Yifan Zhou +6
Large Language Models (LLMs) have shown strong capability in diverse software engineering tasks. However, feature-driven development, a highly prevalent real-world task that involv…
Multi-agent Application System in Office Collaboration Scenarios
Songtao Sun, Jingyi Li, Yuanfei Dong +4
This paper introduces a multi-agent application system designed to enhance office collaboration efficiency and work quality. The system integrates artificial intelligence, machine…
AATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization
Junhui He, Junna Xing, Nan Wang +6
Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. Retrieval-based…
PIPA: Preference Alignment as Prior-Informed Statistical Estimation
Junbo Li, Zhangyang Wang, Qiang Liu
Offline preference alignment for language models such as Direct Preference Optimization (DPO) is favored for its effectiveness and simplicity, eliminating the need for costly reinf…
RAG for Effective Supply Chain Security Questionnaire Automation
Zaynab Batool Reza, Abdul Rafay Syed, Omer Iqbal +4
In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a…
InsightEdit: Towards Better Instruction Following for Image Editing
Yingjing Xu, Jie Kong, Jiazhi Wang +3
In this paper, we focus on the task of instruction-based image editing. Previous works like InstructPix2Pix, InstructDiffusion, and SmartEdit have explored end-to-end editing. Howe…
On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
Kevin Wang, Junbo Li, Neel P. Bhatt +4
Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In t…
Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
Fan Zhou, Zengzhi Wang, Qian Liu +2
Large language model pre-training has traditionally relied on human experts to craft heuristics for improving the corpora quality, resulting in numerous rules developed to date. Ho…