3 citations · 5 across the 25 of their papers we have counts for
7 papers · 2 filters
Silencer: From Discovery to Mitigation of Self-Bias in LLM-as-Benchmark-Generator
Peiwen Yuan, Yiwei Li, Shaoxiong Feng +7
LLM-as-Benchmark-Generator methods have been widely studied as a supplement to human annotators for scalable evaluation, while the potential biases within this paradigm remain unde…
Speculative Decoding for Multi-Sample Inference
Yiwei Li, Jiayi Shi, Shaoxiong Feng +8
We propose a novel speculative decoding method tailored for multi-sample reasoning scenarios, such as self-consistency and Best-of-N sampling. Our method exploits the intrinsic con…
UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +7
Human preference plays a significant role in measuring large language models and guiding them to align with human values. Unfortunately, current comparing-based evaluation (CBE) me…
LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +7
The rapid advancement of large language models (LLMs) has led to a surge in both model supply and application demands. To facilitate effective matching between them, reliable, gene…
Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation
Yiwei Li, Ji Zhang, Shaoxiong Feng +8
Self-consistency improves reasoning by aggregating diverse stochastic samples, yet the dynamics behind its efficacy remain underexplored. We reframe self-consistency as a dynamic d…
From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MARKERGEN
Peiwen Yuan, Chuyi Tan, Shaoxiong Feng +7
Despite the rapid progress of large language models (LLMs), their length-controllable text generation (LCTG) ability remains below expectations, posing a major limitation for pract…
InsBank: Evolving Instruction Subset for Ongoing Alignment
Jiayi Shi, Yiwei Li, Shaoxiong Feng +8
Large language models (LLMs) typically undergo instruction tuning to enhance alignment. Recent studies emphasize that quality and diversity of instruction data are more crucial tha…