6 papers
Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs
Feiyang Li, Shengjing Liu, Qi Zhan +7
As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming increasingly important for quanti…
DySem: Uncovering Dynamic Semantic Components of Large Language Models for Calculating Semantic Textual Similarity
Kaijie Zheng, Weiqin Wang, Yile Wang +1
Calculating semantic textual similarity is a foundational task in natural language processing. Current large language models (LLMs) based methods typically rely on extracting last-…
AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models
Zhanyu Shen, Sijie Cheng, Zhicheng Guo +3
While large language models have achieved remarkable performance in complex tasks, they still need a memory system to utilize historical experience in long-term interactions. Exist…
SemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment
Ziyang Chen, Zhenxuan Huang, Yile Wang +3
Traditional sentence embedding methods employ token-level contrastive learning on non-generative pre-trained models. Recently, there have emerged embedding methods based on generat…
Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning
Weiqin Wang, Yile Wang, Kehao Chen +1
Test-time reinforcement learning mitigates the reliance on annotated data by using majority voting results as pseudo-labels, emerging as a complementary direction to reinforcement…
Ranked Voting based Self-Consistency of Large Language Models
Weiqin Wang, Yile Wang, Hui Huang
Majority voting is considered an effective method to enhance chain-of-thought reasoning, as it selects the answer with the highest "self-consistency" among different reasoning path…