243 citations · 321 across the 22 of their papers we have counts for
22 papers
Reinforcement Learning-based Semi-supervised Knowledge Distillation with LLM-as-a-Judge
Yiyang Shen, Lifu Tu, Weiran Wang
Reinforcement Learning (RL) substantially improves the reasoning capabilities of language models, but most existing RL fine-tuning approaches rely entirely on ground-truth verifiab…
LLM NL2SQL Robustness: Surface Noise vs. Linguistic Variation in Traditional and Agentic Settings
Lifu Tu, Rongguang Wang, Tao Sheng +2
Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conv…
Retrofitting Small Multilingual Models for Retrieval: Matching 7B Performance with 300M Parameters
Lifu Tu, Yingbo Zhou, Semih Yavuz
Training effective multilingual embedding models presents unique challenges due to the diversity of languages and task objectives. Although small multilingual models (<1 B paramete…
Investigating Factuality in Long-Form Text Generation: The Roles of Self-Known and Self-Unknown
Lifu Tu, Rui Meng, Shafiq Joty +2
Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false…
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models
Rui Meng, Ye Liu, Lifu Tu +3
Phrases are fundamental linguistic units through which humans convey semantics. This study critically examines the capacity of API-based large language models (LLMs) to comprehend…
xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations
Can Qin, Congying Xia, Krithika Ramakrishnan +16
We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI'…
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding
Lifu Tu, Semih Yavuz, Jin Qu +4
Large Language Models (LLMs) have demonstrated a powerful ability for text generation. However, achieving optimal results with a given prompt or instruction can be challenging, esp…
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
Lifu Tu, Jin Qu, Semih Yavuz +4
Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rat…
AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
Rui Meng, Ye Liu, Semih Yavuz +6
Dense retrievers have made significant strides in text retrieval and open-domain question answering. However, most of these achievements have relied heavily on extensive human-anno…
Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models
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…
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi +5
Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The…
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP
Lifu Tu
Structured prediction in natural language processing (NLP) has a long history. The complex models of structured application come at the difficulty of learning and inference. These…
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks
Lifu Tu, Tianyu Liu, Kevin Gimpel
Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are…
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Lifu Tu, Garima Lalwani, Spandana Gella +1
Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to…
ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation
Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman +1
We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model. In particular, we view our non-autoregressiv…
Improving Joint Training of Inference Networks and Structured Prediction Energy Networks
Lifu Tu, Richard Yuanzhe Pang, Kevin Gimpel
Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger and McCallum, 2016). Tu and Gimpel (2018) developed an efficient framework for energ…
Generating Diverse Story Continuations with Controllable Semantics
Lifu Tu, Xiaoan Ding, Dong Yu +1
We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story give…
Benchmarking Approximate Inference Methods for Neural Structured Prediction
Lifu Tu, Kevin Gimpel
Exact structured inference with neural network scoring functions is computationally challenging but several methods have been proposed for approximating inference. One approach is…
Learning Approximate Inference Networks for Structured Prediction
Lifu Tu, Kevin Gimpel
Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among pa…
Learning to Embed Words in Context for Syntactic Tasks
Lifu Tu, Kevin Gimpel, Karen Livescu
We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are spec…
Network Inference by Learned Node-Specific Degree Prior
Qingming Tang, Lifu Tu, Weiran Wang +1
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to…