3 citations · 5 across the 10 of their papers we have counts for
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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…
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…
BatchEval: Towards Human-like Text Evaluation
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +4
Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators. However, current sample-wise evaluation paradig…
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
Yiwei Li, Peiwen Yuan, Shaoxiong Feng +5
Large Language Models (LLMs) have performed well on various reasoning tasks, but their inaccessibility and numerous parameters hinder wide application in practice. One promising wa…