9 citations · 26 across the 19 of their papers we have counts for
8 papers · 1 filter
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models
Souvik Das, Lifeng Jin, Linfeng Song +3
Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination -- generating content ungrounded in the realities of training data. Rece…
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…
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation
Xiangci Li, Linfeng Song, Lifeng Jin +3
Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previou…
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…
Inconsistent dialogue responses and how to recover from them
Mian Zhang, Lifeng Jin, Linfeng Song +2
One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem. In this work, we study…