4 citations · 12 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
The Trickle-down Impact of Reward (In-)consistency on RLHF
Lingfeng Shen, Sihao Chen, Linfeng Song +5
Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for…
Friend-training: Learning from Models of Different but Related Tasks
Mian Zhang, Lifeng Jin, Linfeng Song +3
Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differenc…
Distant finetuning with discourse relations for stance classification
Lifeng Jin, Kun Xu, Linfeng Song +1
Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with ind…
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Pei Ke, Haozhe Ji, Yu Ran +5
Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which la…
Semantic Representation for Dialogue Modeling
Xuefeng Bai, Yulong Chen, Linfeng Song +1
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. T…
Domain-Adaptive Pretraining Methods for Dialogue Understanding
Han Wu, Kun Xu, Linfeng Song +3
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adapt…
Video-aided Unsupervised Grammar Induction
Songyang Zhang, Linfeng Song, Lifeng Jin +3
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induc…
Conversational Semantic Role Labeling
Kun Xu, Han Wu, Linfeng Song +3
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every sin…