3 citations · 5 across the 23 of their papers we have counts for
26 papers
Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling
Xinglin Wang, Hao Lin, Shaoxiong Feng +9
Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing par…
On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows
Xinglin Wang, Zishen Liu, Shaoxiong Feng +9
Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or tools and coordinated according t…
Learning More from Less: Unlocking Internal Representations for Benchmark Compression
Yueqi Zhang, Jin Hu, Shaoxiong Feng +9
The prohibitive cost of evaluating Large Language Models (LLMs) necessitates efficient alternatives to full-scale benchmarking. Prevalent approaches address this by identifying a s…
Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling
Xinglin Wang, Jiayi Shi, Shaoxiong Feng +8
Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration of the solution space. However,…
Breaking the Self-Confirming Loop: Diagnosing and Mitigating Systemic Reward Bias in Self-Rewarding RL
Chuyi Tan, Peiwen Yuan, Xinglin Wang +8
Reinforcement learning with verifiable rewards (RLVR) efficiently scales the reasoning ability of large language models (LLMs) but is bottlenecked by scarce labeled data. Reinforce…
PatternKV: Flattening KV Representation Expands Quantization Headroom
Ji Zhang, Yiwei Li, Shaoxiong Feng +8
KV cache in autoregressive LLMs eliminates redundant recomputation but has emerged as the dominant memory and bandwidth bottleneck during inference, notably with long contexts and…
Every Rollout Counts: Optimal Resource Allocation for Efficient Test-Time Scaling
Xinglin Wang, Yiwei Li, Shaoxiong Feng +7
Test-Time Scaling (TTS) improves the performance of Large Language Models (LLMs) by using additional inference-time computation to explore multiple reasoning paths through search.…
Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules
Yueqi Zhang, Peiwen Yuan, Shaoxiong Feng +7
Human-AI conversation frequently relies on quoting earlier text-"check it with the formula I just highlighted"-yet today's large language models (LLMs) lack an explicit mechanism f…
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…
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation
Peiwen Yuan, Yueqi Zhang, Shaoxiong Feng +7
Evaluating models on large benchmarks is very resource-intensive, especially during the period of rapid model evolution. Existing efficient evaluation methods estimate the performa…
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…
Instruction Embedding: Latent Representations of Instructions Towards Task Identification
Yiwei Li, Jiayi Shi, Shaoxiong Feng +6
Instruction data is crucial for improving the capability of Large Language Models (LLMs) to align with human-level performance. Recent research LIMA demonstrates that alignment is…
Focused Large Language Models are Stable Many-Shot Learners
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +7
In-Context Learning (ICL) enables large language models (LLMs) to achieve rapid task adaptation by learning from demonstrations. With the increase in available context length of LL…
Poor-Supervised Evaluation for SuperLLM via Mutual Consistency
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +5
The guidance from capability evaluations has greatly propelled the progress of both human society and Artificial Intelligence. However, as LLMs evolve, it becomes challenging to co…
Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning
Xinglin Wang, Shaoxiong Feng, Yiwei Li +6
Self-consistency (SC), a widely used decoding strategy for chain-of-thought reasoning, shows significant gains across various multi-step reasoning tasks but comes with a high cost…
CogLM: Tracking Cognitive Development of Large Language Models
Xinglin Wang, Peiwen Yuan, Shaoxiong Feng +5
Piaget's Theory of Cognitive Development (PTC) posits that the development of cognitive levels forms the foundation for human learning across various abilities. As Large Language M…
Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation
Xinglin Wang, Yiwei Li, Shaoxiong Feng +5
Self-consistency (SC), leveraging multiple samples from LLMs, shows significant gains on various reasoning tasks but struggles with free-form generation due to the difficulty of ag…
Generative Dense Retrieval: Memory Can Be a Burden
Peiwen Yuan, Xinglin Wang, Shaoxiong Feng +5
Generative Retrieval (GR), autoregressively decoding relevant document identifiers given a query, has been shown to perform well under the setting of small-scale corpora. By memori…
Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
Yiwei Li, Peiwen Yuan, Shaoxiong Feng +5
Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step…