Search Results for author: Caiming Xiong

Found 285 papers, 150 papers with code

[CASPI] Causal-aware Safe Policy Improvement for Task-oriented Dialogue

no code implementations ACL 2022 Govardana Sachithanandam Ramachandran, Kazuma Hashimoto, Caiming Xiong

Further more we demonstrate sample efficiency, where our method trained only on 20% of the data, are comparable to current state of the art method trained on 100% data on two out of there evaluation metrics.

Dialogue Management Management +1

DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents

1 code implementation COLING 2022 Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, ran Xu, Caiming Xiong

Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form.

document understanding Language Modelling +1

The Thieves on Sesame Street are Polyglots - Extracting Multilingual Models from Monolingual APIs

no code implementations EMNLP 2020 Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher

Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim{'}s labels for that data.

Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging

no code implementations EMNLP 2020 Semih Yavuz, Kazuma Hashimoto, Wenhao Liu, Nitish Shirish Keskar, Richard Socher, Caiming Xiong

The concept of Dialogue Act (DA) is universal across different task-oriented dialogue domains - the act of {``}request{''} carries the same speaker intention whether it is for restaurant reservation or flight booking.

Data Augmentation Domain Generalization

Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label

no code implementations EMNLP (NLP4ConvAI) 2021 Jin Qu, Kazuma Hashimoto, Wenhao Liu, Caiming Xiong, Yingbo Zhou

Compared with DNNC, our proposed method is more efficient in both training and serving since it is based upon the entailment between query utterance and labels instead of all the training examples.

Classification intent-classification +2

Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

1 code implementation15 Jul 2024 Ruisheng Cao, Fangyu Lei, Haoyuan Wu, Jixuan Chen, Yeqiao Fu, Hongcheng Gao, Xinzhuang Xiong, Hanchong Zhang, Yuchen Mao, Wenjing Hu, Tianbao Xie, Hongshen Xu, Danyang Zhang, Sida Wang, Ruoxi Sun, Pengcheng Yin, Caiming Xiong, Ansong Ni, Qian Liu, Victor Zhong, Lu Chen, Kai Yu, Tao Yu

These tasks, derived from real-world use cases, evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.

Code Generation

Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems

1 code implementation1 Jul 2024 Philippe Laban, Alexander R. Fabbri, Caiming Xiong, Chien-Sheng Wu

The "Summary of a Haystack" (SummHay) task then requires a system to process the Haystack and generate, given a query, a summary that identifies the relevant insights and precisely cites the source documents.

RAG

INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness

no code implementations23 Jun 2024 Hung Le, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Doyen Sahoo

In this work, we introduce INDICT: a new framework that empowers LLMs with Internal Dialogues of Critiques for both safety and helpfulness guidance.

Code Generation Navigate

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens

1 code implementation17 Jun 2024 Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Matt Jordan, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, ran Xu, Yejin Choi, Ludwig Schmidt

Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs).

RLHF Workflow: From Reward Modeling to Online RLHF

3 code implementations13 May 2024 Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang

We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature.

Chatbot Language Modelling +1

Investigating the prompt leakage effect and black-box defenses for multi-turn LLM interactions

no code implementations24 Apr 2024 Divyansh Agarwal, Alexander R. Fabbri, Philippe Laban, Ben Risher, Shafiq Joty, Caiming Xiong, Chien-Sheng Wu

In a multi-turn setting, our threat model elevates the average attack success rate (ASR) to 86. 2%, including a 99% leakage with GPT-4 and claude-1. 3.

RAG

OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

no code implementations11 Apr 2024 Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, Tao Yu

Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity.

Benchmarking

What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases

1 code implementation3 Apr 2024 Anthony Meng Huat Tiong, Junqi Zhao, Boyang Li, Junnan Li, Steven C. H. Hoi, Caiming Xiong

Vision-language (VL) models, pretrained on colossal image-text datasets, have attained broad VL competence that is difficult to evaluate.

Transfer Learning

How Much are LLMs Contaminated? A Comprehensive Survey and the LLMSanitize Library

1 code implementation31 Mar 2024 Mathieu Ravaut, Bosheng Ding, Fangkai Jiao, Hailin Chen, Xingxuan Li, Ruochen Zhao, Chengwei Qin, Caiming Xiong, Shafiq Joty

With the rise of Large Language Models (LLMs) in recent years, new opportunities are emerging, but also new challenges, and contamination is quickly becoming critical.

Question Answering

FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability

1 code implementation28 Feb 2024 Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, ran Xu, Wenpeng Yin, Caiming Xiong

This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents.

AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning

2 code implementations23 Feb 2024 JianGuo Zhang, Tian Lan, Rithesh Murthy, Zhiwei Liu, Weiran Yao, Juntao Tan, Thai Hoang, Liangwei Yang, Yihao Feng, Zuxin Liu, Tulika Awalgaonkar, Juan Carlos Niebles, Silvio Savarese, Shelby Heinecke, Huan Wang, Caiming Xiong

It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training.

AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System

1 code implementation23 Feb 2024 Zhiwei Liu, Weiran Yao, JianGuo Zhang, Liangwei Yang, Zuxin Liu, Juntao Tan, Prafulla K. Choubey, Tian Lan, Jason Wu, Huan Wang, Shelby Heinecke, Caiming Xiong, Silvio Savarese

Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease.

AI Agent

Unified Training of Universal Time Series Forecasting Transformers

1 code implementation4 Feb 2024 Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.

Time Series Time Series Forecasting

Causal Layering via Conditional Entropy

no code implementations19 Jan 2024 Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese

Under appropriate assumptions and conditioning, we can separate the sources or sinks from the remainder of the nodes by comparing their conditional entropy to the unconditional entropy of their noise.

Causal Discovery

Editing Arbitrary Propositions in LLMs without Subject Labels

no code implementations15 Jan 2024 Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese

On datasets of binary propositions derived from the CounterFact dataset, we show that our method -- without access to subject labels -- performs close to state-of-the-art L\&E methods which has access subject labels.

Language Modelling Large Language Model +1

Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions

1 code implementation3 Jan 2024 David Junhao Zhang, Dongxu Li, Hung Le, Mike Zheng Shou, Caiming Xiong, Doyen Sahoo

This work presents Moonshot, a new video generation model that conditions simultaneously on multimodal inputs of image and text.

Image Animation Video Editing +1

X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning

1 code implementation30 Nov 2023 Artemis Panagopoulou, Le Xue, Ning Yu, Junnan Li, Dongxu Li, Shafiq Joty, ran Xu, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles

Vision-language pre-training and instruction tuning have demonstrated general-purpose capabilities in 2D visual reasoning tasks by aligning visual encoders with state-of-the-art large language models (LLMs).

Visual Reasoning

Diffusion Model Alignment Using Direct Preference Optimization

1 code implementation CVPR 2024 Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik

Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences.

Lexical Repetitions Lead to Rote Learning: Unveiling the Impact of Lexical Overlap in Train and Test Reference Summaries

no code implementations15 Nov 2023 Prafulla Kumar Choubey, Alexander R. Fabbri, Caiming Xiong, Chien-Sheng Wu

Ideal summarization models should generalize to novel summary-worthy content without remembering reference training summaries by rote.

Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

no code implementations14 Nov 2023 Philippe Laban, Lidiya Murakhovs'ka, Caiming Xiong, Chien-Sheng Wu

The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs remains limited.

Fair Abstractive Summarization of Diverse Perspectives

1 code implementation14 Nov 2023 Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang

However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization.

Abstractive Text Summarization Fairness

OpenAgents: An Open Platform for Language Agents in the Wild

2 code implementations16 Oct 2023 Tianbao Xie, Fan Zhou, Zhoujun Cheng, Peng Shi, Luoxuan Weng, Yitao Liu, Toh Jing Hua, Junning Zhao, Qian Liu, Che Liu, Leo Z. Liu, Yiheng Xu, Hongjin Su, Dongchan Shin, Caiming Xiong, Tao Yu

Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).

2D Object Detection

How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations

no code implementations16 Oct 2023 Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai

Through extensive probing and a new pasting experiment, we further reveal several mechanisms within the trained transformers, such as concrete copying behaviors on both the inputs and the representations, linear ICL capability of the upper layers alone, and a post-ICL representation selection mechanism in a harder mixture setting.

In-Context Learning

Lemur: Harmonizing Natural Language and Code for Language Agents

1 code implementation10 Oct 2023 Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, Tao Yu

We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.

L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models

no code implementations29 Sep 2023 Ansong Ni, Pengcheng Yin, Yilun Zhao, Martin Riddell, Troy Feng, Rui Shen, Stephen Yin, Ye Liu, Semih Yavuz, Caiming Xiong, Shafiq Joty, Yingbo Zhou, Dragomir Radev, Arman Cohan

Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a few-shot or even zero-shot manner.

Code Generation Math +1

Beyond the Chat: Executable and Verifiable Text-Editing with LLMs

no code implementations27 Sep 2023 Philippe Laban, Jesse Vig, Marti A. Hearst, Caiming Xiong, Chien-Sheng Wu

Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing.

XGen-7B Technical Report

1 code implementation7 Sep 2023 Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryściński, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong

Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.

2k 8k

Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System

no code implementations16 Aug 2023 JianGuo Zhang, Stephen Roller, Kun Qian, Zhiwei Liu, Rui Meng, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capabilities of pre-trained models.

Natural Language Understanding Retrieval +1

Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization

1 code implementation4 Aug 2023 Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, JianGuo Zhang, Devansh Arpit, ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese

This demonstrates that using policy gradient optimization to improve language agents, for which we believe our work is one of the first, seems promising and can be applied to optimize other models in the agent architecture to enhance agent performances over time.

Language Modelling

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI

1 code implementation19 Jul 2023 JianGuo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness.

Diversity Few-Shot Learning +2

Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight

no code implementations6 Jul 2023 Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai

This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case.

Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

1 code implementation1 Jun 2023 Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, Chien-Sheng Jason Wu

Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks.

SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages

1 code implementation30 May 2023 Philippe Laban, Jesse Vig, Wojciech Kryscinski, Shafiq Joty, Caiming Xiong, Chien-Sheng Wu

Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context.

Sentence Text Simplification

LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond

1 code implementation23 May 2023 Philippe Laban, Wojciech Kryściński, Divyansh Agarwal, Alexander R. Fabbri, Caiming Xiong, Shafiq Joty, Chien-Sheng Wu

To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.

Misinformation

HPE:Answering Complex Questions over Text by Hybrid Question Parsing and Execution

no code implementations12 May 2023 Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou

It comprises two central pillars: (1) We parse the question of varying complexity into an intermediate representation, named H-expression, which is composed of simple questions as the primitives and symbolic operations representing the relationships among them; (2) To execute the resulting H-expressions, we design a hybrid executor, which integrates the deterministic rules to translate the symbolic operations with a drop-in neural reader network to answer each decomposed simple question.

Knowledge Graphs Question Answering +1

Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training

no code implementations12 May 2023 Ziwei Fan, Zhiwei Liu, Shelby Heinecke, JianGuo Zhang, Huan Wang, Caiming Xiong, Philip S. Yu

This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs.

Recommendation Systems

CodeGen2: Lessons for Training LLMs on Programming and Natural Languages

2 code implementations3 May 2023 Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, Yingbo Zhou

In this study, we attempt to render the training of LLMs for program synthesis more efficient by unifying four key components: (1) model architectures, (2) learning methods, (3) infill sampling, and, (4) data distributions.

Causal Language Modeling Decoder +3

Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning

1 code implementation3 Apr 2023 Lifu Tu, Jin Qu, Semih Yavuz, Shafiq Joty, Wenhao Liu, Caiming Xiong, Yingbo Zhou

Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings.

Cross-Lingual Transfer intent-classification +4

GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation

1 code implementation ICCV 2023 Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, ran Xu

Empirical results show that GlueNet can be trained efficiently and enables various capabilities beyond previous state-of-the-art models: 1) multilingual language models such as XLM-Roberta can be aligned with existing T2I models, allowing for the generation of high-quality images from captions beyond English; 2) GlueNet can align multi-modal encoders such as AudioCLIP with the Stable Diffusion model, enabling sound-to-image generation; 3) it can also upgrade the current text encoder of the latent diffusion model for challenging case generation.

Decoder Image Generation

On the Unlikelihood of D-Separation

no code implementations10 Mar 2023 Itai Feigenbaum, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Devansh Arpit

We then provide an analytic average case analysis of the PC Algorithm for causal discovery, as well as a variant of the SGS Algorithm we call UniformSGS.

Causal Discovery

Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation

1 code implementation7 Mar 2023 Yixin Liu, Alexander R. Fabbri, Yilun Zhao, PengFei Liu, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev

Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics.

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions

no code implementations17 Feb 2023 Philippe Laban, Chien-Sheng Wu, Lidiya Murakhovs'ka, Xiang 'Anthony' Chen, Caiming Xiong

In a second usability study, we developed and implemented a reading exercise with 95 novice news readers to measure exposure to coverage diversity.

Diversity

Improved Online Conformal Prediction via Strongly Adaptive Online Learning

2 code implementations15 Feb 2023 Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai

We prove that our methods achieve near-optimal strongly adaptive regret for all interval lengths simultaneously, and approximately valid coverage.

Conformal Prediction Image Classification +4

Lower Bounds for Learning in Revealing POMDPs

no code implementations2 Feb 2023 Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai

However, the fundamental limits for learning in revealing POMDPs are much less understood, with existing lower bounds being rather preliminary and having substantial gaps from the current best upper bounds.

Reinforcement Learning (RL)

Hierarchical Point Attention for Indoor 3D Object Detection

no code implementations6 Jan 2023 Manli Shu, Le Xue, Ning Yu, Roberto Martín-Martín, Caiming Xiong, Tom Goldstein, Juan Carlos Niebles, ran Xu

By plugging our proposed modules into the state-of-the-art transformer-based 3D detectors, we improve the previous best results on both benchmarks, with more significant improvements on smaller objects.

3D Object Detection Object +1

Best-$k$ Search Algorithm for Neural Text Generation

no code implementations22 Nov 2022 Jiacheng Xu, Caiming Xiong, Silvio Savarese, Yingbo Zhou

We first investigate the vanilla best-first search (BFS) algorithm and then propose the Best-$k$ Search algorithm.

Diversity Question Generation +3

SPE: Symmetrical Prompt Enhancement for Fact Probing

no code implementations14 Nov 2022 Yiyuan Li, Tong Che, Yezhen Wang, Zhengbao Jiang, Caiming Xiong, Snigdha Chaturvedi

In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction.

Object

Improving Factual Consistency in Summarization with Compression-Based Post-Editing

1 code implementation11 Nov 2022 Alexander R. Fabbri, Prafulla Kumar Choubey, Jesse Vig, Chien-Sheng Wu, Caiming Xiong

We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed.

Informativeness Sentence +1

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database

no code implementations9 Nov 2022 Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou

Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB).

Question Answering Semantic Parsing

Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning

no code implementations23 Oct 2022 Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey, Chien-Sheng Wu, Caiming Xiong

Through this way, SESoM inherits the superior generalization of model ensemble approaches and simultaneously captures the sample-specific competence of each source prompt.

Transfer Learning

Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models

2 code implementations22 Oct 2022 Lifu Tu, Caiming Xiong, Yingbo Zhou

Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks.

Cross-Lingual Transfer Natural Language Understanding +3

Binding Language Models in Symbolic Languages

2 code implementations6 Oct 2022 Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu

We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e. g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations.

Language Modelling Semantic Parsing +1

Generating Negative Samples for Sequential Recommendation

no code implementations7 Aug 2022 Yongjun Chen, Jia Li, Zhiwei Liu, Nitish Shirish Keskar, Huan Wang, Julian McAuley, Caiming Xiong

Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative.

Sequential Recommendation

BigIssue: A Realistic Bug Localization Benchmark

no code implementations21 Jul 2022 Paul Kassianik, Erik Nijkamp, Bo Pang, Yingbo Zhou, Caiming Xiong

As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code?

BIG-bench Machine Learning Diversity +1

Policy Optimization for Markov Games: Unified Framework and Faster Convergence

no code implementations6 Jun 2022 Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai

Next, we show that this framework instantiated with the Optimistic Follow-The-Regularized-Leader (OFTRL) algorithm at each state (and smooth value updates) can find an $\mathcal{\widetilde{O}}(T^{-5/6})$ approximate NE in $T$ iterations, and a similar algorithm with slightly modified value update rule achieves a faster $\mathcal{\widetilde{O}}(T^{-1})$ convergence rate.

Multi-agent Reinforcement Learning

MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation

1 code implementation31 May 2022 Wenzhuo Yang, Jia Li, Caiming Xiong, Steven C. H. Hoi

Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions.

BIG-bench Machine Learning counterfactual +1

Modeling Multi-hop Question Answering as Single Sequence Prediction

no code implementations ACL 2022 Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou, Nitish Shirish Keskar, Caiming Xiong

Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA.

Answer Generation Decoder +4

OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval

no code implementations Findings (ACL) 2022 Tong Niu, Kazuma Hashimoto, Yingbo Zhou, Caiming Xiong

When finetuned on a single rich-resource language pair, be it English-centered or not, our model is able to match the performance of the ones finetuned on all language pairs under the same data budget with less than 2. 0 points decrease in accuracy.

Machine Translation Retrieval +3

Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets

1 code implementation13 May 2022 Philippe Laban, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong

Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model's output over another is often necessary.

nlg evaluation Question Answering +3

Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework

1 code implementation CVPR 2022 Shu Zhang, ran Xu, Caiming Xiong, Chetan Ramaiah

Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks.

Contrastive Learning Representation Learning

A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis

1 code implementation Findings (NAACL) 2022 Ehsan Hosseini-Asl, Wenhao Liu, Caiming Xiong

Our evaluation results on the single-task polarity prediction show that our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4

ELECRec: Training Sequential Recommenders as Discriminators

1 code implementation5 Apr 2022 Yongjun Chen, Jia Li, Caiming Xiong

A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training.

Sequential Recommendation

CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

6 code implementations25 Mar 2022 Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong

To democratize this, we train and release a family of large language models up to 16. 1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER.

Code Generation Language Modelling +2

Improving Contrastive Learning with Model Augmentation

1 code implementation25 Mar 2022 Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong

However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.

Contrastive Learning Data Augmentation +2

ConTinTin: Continual Learning from Task Instructions

no code implementations ACL 2022 Wenpeng Yin, Jia Li, Caiming Xiong

This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction.

Continual Learning

Long Document Summarization with Top-down and Bottom-up Inference

no code implementations15 Mar 2022 Bo Pang, Erik Nijkamp, Wojciech Kryściński, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents.

Structure Extraction in Task-Oriented Dialogues with Slot Clustering

2 code implementations28 Feb 2022 Liang Qiu, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong

Extracting structure information from dialogue data can help us better understand user and system behaviors.

Clustering Data Augmentation +1

Efficient and Differentiable Conformal Prediction with General Function Classes

1 code implementation ICLR 2022 Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong

Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly over existing approaches in several applications such as prediction intervals with improved length, minimum-volume prediction sets for multi-output regression, and label prediction sets for image classification.

Conformal Prediction Image Classification +2

Intent Contrastive Learning for Sequential Recommendation

1 code implementation5 Feb 2022 Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, Caiming Xiong

Specifically, we introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering.

Contrastive Learning Model Optimization +3

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

6 code implementations28 Jan 2022 Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi

Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision.

Ranked #3 on Open Vocabulary Attribute Detection on OVAD-Box benchmark (using extra training data)

Image Captioning Image-text matching +5

RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems

1 code implementation12 Jan 2022 Zohreh Ovaisi, Shelby Heinecke, Jia Li, Yongfeng Zhang, Elena Zheleva, Caiming Xiong

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data.

Recommendation Systems

Value Retrieval with Arbitrary Queries for Form-like Documents

1 code implementation15 Dec 2021 Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, ran Xu, Caiming Xiong

Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form.

document understanding Language Modelling +1

Open Vocabulary Object Detection with Pseudo Bounding-Box Labels

1 code implementation18 Nov 2021 Mingfei Gao, Chen Xing, Juan Carlos Niebles, Junnan Li, ran Xu, Wenhao Liu, Caiming Xiong

To enlarge the set of base classes, we propose a method to automatically generate pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs.

Object object-detection +1

Dense Hierarchical Retrieval for Open-Domain Question Answering

1 code implementation Findings (EMNLP) 2021 Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip S. Yu

In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.

Open-Domain Question Answering Text Retrieval

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

1 code implementation21 Oct 2021 Devansh Arpit, Huan Wang, Yingbo Zhou, Caiming Xiong

We first show that this chaotic behavior exists even along the training optimization trajectory of a single model, and propose a simple model averaging protocol that both significantly boosts domain generalization and diminishes the impact of stochasticity by improving the rank correlation between the in-domain validation accuracy and out-domain test accuracy, which is crucial for reliable early stopping.

Domain Generalization Model Selection

Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles

no code implementations19 Oct 2021 Bram Wallace, Devansh Arpit, Huan Wang, Caiming Xiong

Pretraining convolutional neural networks via self-supervision, and applying them in transfer learning, is an incredibly fast-growing field that is rapidly and iteratively improving performance across practically all image domains.

Transfer Learning

Improving Tail-Class Representation with Centroid Contrastive Learning

no code implementations19 Oct 2021 Anthony Meng Huat Tiong, Junnan Li, Guosheng Lin, Boyang Li, Caiming Xiong, Steven C. H. Hoi

ICCL interpolates two images from a class-agnostic sampler and a class-aware sampler, and trains the model such that the representation of the interpolative image can be used to retrieve the centroids for both source classes.

Contrastive Learning Image Classification +2

Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE

no code implementations19 Oct 2021 Devansh Arpit, Aadyot Bhatnagar, Huan Wang, Caiming Xiong

Wasserstein autoencoder (WAE) shows that matching two distributions is equivalent to minimizing a simple autoencoder (AE) loss under the constraint that the latent space of this AE matches a pre-specified prior distribution.

Contrastive Learning Representation Learning

Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting

no code implementations11 Oct 2021 Zahra Fatemi, Chen Xing, Wenhao Liu, Caiming Xiong

In this work, we empirically show that catastrophic forgetting occurs in such methods by evaluating them with general NLP tasks in GLUE.

coreference-resolution Fairness

Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks

1 code implementation8 Oct 2021 Le Xue, Mingfei Gao, Zeyuan Chen, Caiming Xiong, ran Xu

We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks.

Optical Character Recognition (OCR)

Long Document Summarization with Top-Down and Bottom-Up Representation Inference

no code implementations29 Sep 2021 Bo Pang, Erik Nijkamp, Wojciech Maciej Kryscinski, Silvio Savarese, Yingbo Zhou, Caiming Xiong

Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens in the source documents.

Document Summarization

Self-supervised Learning for Sequential Recommendation with Model Augmentation

no code implementations29 Sep 2021 Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong

However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.

Contrastive Learning Data Augmentation +2

Modeling Dynamic Attributes for Next Basket Recommendation

no code implementations23 Sep 2021 Yongjun Chen, Jia Li, Chenghao Liu, Chenxi Li, Markus Anderle, Julian McAuley, Caiming Xiong

However, properly integrating them into user interest models is challenging since attribute dynamics can be diverse such as time-interval aware, periodic patterns (etc.

Attribute Next-basket recommendation

RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

1 code implementation ACL 2022 Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou, Caiming Xiong

We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability.

Entity Linking Knowledge Base Question Answering +1

Contrastive Self-supervised Sequential Recommendation with Robust Augmentation

1 code implementation14 Aug 2021 Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, Caiming Xiong

In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.

Contrastive Learning Self-Supervised Learning +1

A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning

no code implementations NeurIPS 2021 Pan Zhou, Caiming Xiong, Xiao-Tong Yuan, Steven Hoi

Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query.

Contrastive Learning Representation Learning +2

Understanding the Under-Coverage Bias in Uncertainty Estimation

no code implementations NeurIPS 2021 Yu Bai, Song Mei, Huan Wang, Caiming Xiong

Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input.

quantile regression

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning

no code implementations NeurIPS 2021 Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai

This offline result is the first that matches the sample complexity lower bound in this setting, and resolves a recent open question in offline RL.

Offline RL Open-Ended Question Answering +2

Evaluating State-of-the-Art Classification Models Against Bayes Optimality

1 code implementation NeurIPS 2021 Ryan Theisen, Huan Wang, Lav R. Varshney, Caiming Xiong, Richard Socher

Moreover, we show that by varying the temperature of the learned flow models, we can generate synthetic datasets that closely resemble standard benchmark datasets, but with almost any desired Bayes error.

Unsupervised Out-of-Domain Detection via Pre-trained Transformers

1 code implementation ACL 2021 Keyang Xu, Tongzheng Ren, Shikun Zhang, Yihao Feng, Caiming Xiong

Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs.

BookSum: A Collection of Datasets for Long-form Narrative Summarization

2 code implementations18 May 2021 Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, Dragomir Radev

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases.

Abstractive Text Summarization

QAConv: Question Answering on Informative Conversations

1 code implementation ACL 2022 Chien-Sheng Wu, Andrea Madotto, Wenhao Liu, Pascale Fung, Caiming Xiong

This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source.

Question Answering

Pseudo Siamese Network for Few-shot Intent Generation

no code implementations3 May 2021 Congying Xia, Caiming Xiong, Philip Yu

PSN consists of two identical subnetworks with the same structure but different weights: an action network and an object network.

Intent Detection Object +1

Learning to Synthesize Data for Semantic Parsing

1 code implementation NAACL 2021 Bailin Wang, Wenpeng Yin, Xi Victoria Lin, Caiming Xiong

Moreover, explicitly modeling compositions using PCFG leads to a better exploration of unseen programs, thus generate more diverse data.

Domain Generalization Semantic Parsing +3

FeTaQA: Free-form Table Question Answering

1 code implementation1 Apr 2021 Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Nick Schoelkopf, Riley Kong, Xiangru Tang, Murori Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev

Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.

Question Answering Retrieval +2

Causal-aware Safe Policy Improvement for Task-oriented dialogue

1 code implementation10 Mar 2021 Govardana Sachithanandam Ramachandran, Kazuma Hashimoto, Caiming Xiong

This method gives guarantees on dialogue policy's performance and also learns to shape rewards according to intentions behind human responses, rather than just mimicking demonstration data; this couple with batch-RL helps overall with sample efficiency of the framework.

Dialogue Management Management +1

Structured Scene Memory for Vision-Language Navigation

1 code implementation CVPR 2021 Hanqing Wang, Wenguan Wang, Wei Liang, Caiming Xiong, Jianbing Shen

Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i. e., entailing an agent to navigate 3D environments through following linguistic instructions.

Decision Making Navigate +1

Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games

no code implementations NeurIPS 2021 Yu Bai, Chi Jin, Huan Wang, Caiming Xiong

Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum.