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Found 5 papers, 1 papers with code
Exploring Task Unification in Graph Representation Learning via Generative Approach
Specifically, GA^2E proposes to use the subgraph as the meta-structure, which remains consistent across all graph tasks (ranging from node-, edge-, and graph-level to transfer learning) and all stages (both during training and inference).
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint
To address it, we propose a new RL method named \textbf{RLMEC} that incorporates a generative model as the reward model, which is trained by the erroneous solution rewriting task under the minimum editing constraint, and can produce token-level rewards for RL training.
Ask One More Time: Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality.
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
Thus, we propose GraphRank, a simple yet efficient graph contrastive learning method that addresses the problem of false negative samples by redefining the concept of negative samples to a certain extent, thereby avoiding the issue of false negative samples.
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning.
Ranked #87 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
