1 citations · 1 across the 8 of their papers we have counts for
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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…
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…
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…
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…