4 citations · 11 across the 13 of their papers we have counts for
10 papers · 1 filter
Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learning
Xiyao Wang, Linfeng Song, Ye Tian +5
Monte Carlo Tree Search (MCTS) has recently emerged as a powerful technique for enhancing the reasoning capabilities of LLMs. Techniques such as SFT or DPO have enabled LLMs to dis…
SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models
Dian Yu, Baolin Peng, Ye Tian +3
There is a growing trend of teaching large language models (LLMs) to solve mathematical problems through coding. Existing studies primarily focus on prompting powerful, closed-sour…
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
Yuheng Zhang, Dian Yu, Baolin Peng +6
Reinforcement Learning with Human Feedback (RLHF) has achieved great success in aligning large language models (LLMs) with human preferences. Prevalent RLHF approaches are reward-b…
LiteSearch: Efficacious Tree Search for LLM
Ante Wang, Linfeng Song, Ye Tian +5
Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they of…
Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching
Xiaoying Zhang, Baolin Peng, Ye Tian +4
Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current,…
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Ye Tian, Baolin Peng, Linfeng Song +4
Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Recent work p…
Self-Consistency Boosts Calibration for Math Reasoning
Ante Wang, Linfeng Song, Ye Tian +5
Calibration, which establishes the correlation between accuracy and model confidence, is important for LLM development. We design three off-the-shelf calibration methods based on s…
Collaborative decoding of critical tokens for boosting factuality of large language models
Lifeng Jin, Baolin Peng, Linfeng Song +3
The most common training pipeline for large language models includes pretraining, finetuning and aligning phases, with their respective resulting models, such as the pretrained mod…
Fine-Grained Self-Endorsement Improves Factuality and Reasoning
Ante Wang, Linfeng Song, Baolin Peng +5
This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. Particularly, we propose a self-endorsement fram…
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
Xiaoying Zhang, Baolin Peng, Ye Tian +5
Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e. "hallucinations", even when they hold relevant knowle…