Publications (37)
Unsupervised Multi-Granularity Summarization
Ming Zhong, Yang Liu, Suyu Ge +7
Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, gr…
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu +9
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramou…
Your thoughts tell who you are: Characterize the reasoning patterns of LRMs
Yida Chen, Yuning Mao, Xianjun Yang +7
Current comparisons of large reasoning models (LRMs) focus on macro-level statistics such as task accuracy or reasoning length. Whether different LRMs reason differently remains an…
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Hakan Inan, Kartikeya Upasani, Jianfeng Chi +8
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases. Our model incorporates a safety risk taxonomy, a valuable tool f…
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
Yuning Mao, Tong Zhao, Andrey Kan +4
Taxonomies have found wide applications in various domains, especially online for item categorization, browsing, and search. Despite the prevalent use of online catalog taxonomies,…
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning
Yuning Mao, Lambert Mathias, Rui Hou +5
Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when…
Generation-Augmented Retrieval for Open-domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu +4
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts wi…
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
Yuxin Xiao, Zecheng Zhang, Yuning Mao +2
Stepping from sentence-level to document-level, the research on relation extraction (RE) confronts increasing text length and more complicated entity interactions. Consequently, it…
Discretizing Reward Models
Vijay Viswanathan, Shiqi Wang, Devamanyu Hazarika +4
Despite their widespread use, the role of reward models in shaping reinforcement learning is poorly understood. Reward models offer a tempting promise: they automatically estimate…
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +4
Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft pro…
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
Yuning Mao, Xiang Ren, Jiaming Shen +2
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e.,…
CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision
Yuning Mao, Ming Zhong, Jiawei Han
Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the…
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Jaehyung Kim, Yuning Mao, Rui Hou +7
Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversa…
Representation Deficiency in Masked Language Modeling
Yu Meng, Jitin Krishnan, Sinong Wang +6
Masked Language Modeling (MLM) has been one of the most prominent approaches for pretraining bidirectional text encoders due to its simplicity and effectiveness. One notable concer…
LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu +12
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
Davis Liang, Hila Gonen, Yuning Mao +5
Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size…
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Yuning Mao, Xiang Ren, Heng Ji +1
Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document. In this paper, we pro…
Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu +4
Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the ret…
Improving Model Factuality with Fine-grained Critique-based Evaluator
Yiqing Xie, Wenxuan Zhou, Pradyot Prakash +9
Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuali…
Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone +65
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our f…
Progressive Prompts: Continual Learning for Language Models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +3
We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting…
Taxonomy Completion via Triplet Matching Network
Jieyu Zhang, Xiangchen Song, Ying Zeng +4
Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concep…
Facet-Aware Evaluation for Extractive Summarization
Yuning Mao, Liyuan Liu, Qi Zhu +2
Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of…
Generating Representative Headlines for News Stories
Xiaotao Gu, Yuning Mao, Jiawei Han +7
Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is…
Towards a Unified Multi-Dimensional Evaluator for Text Generation
Ming Zhong, Yang Liu, Da Yin +6
Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimen…
Dr. Zero: Self-Evolving Search Agents without Training Data
Zhenrui Yue, Kartikeya Upasani, Xianjun Yang +5
As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm. This approach allows large language…
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
Deren Lei, Gangrong Jiang, Xiaotao Gu +3
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward…
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder
Xianjun Yang, Shaoliang Nie, Lijuan Liu +5
Instruction tuning data are often quantity-saturated due to the large volume of data collection and fast model iteration, leaving data selection important but underexplored. Existi…
Hierarchical Text Classification with Reinforced Label Assignment
Yuning Mao, Jingjing Tian, Jiawei Han +1
While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or comp…
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Yuning Mao, Yanru Qu, Yiqing Xie +2
While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (M…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
Yuning Mao, Wenchang Ma, Deren Lei +2
Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, wit…
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning
Tim Franzmeyer, Archie Sravankumar, Lijuan Liu +6
Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -- a problem known as hallucinati…
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion
Yiqing Xie, Jiaming Shen, Sha Li +2
Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. Typical DocRE methods blindly take the full document as input, while…
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Suyu Ge, Chunting Zhou, Rui Hou +5
Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressin…
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Xin Luna Dong, Xiang He, Andrey Kan +19
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question a…
MPT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Taowen Wang, Yiyang Liu, James Chenhao Liang +11
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization cap…