15 citations · 26 across the 21 of their papers we have counts for
24 papers
Reasoning Jury: Multi-Model Consensus for Evaluating Reasoning Traces
Congchao Wang, Diwakar Singh, Qiaozi Gao +3
Improving reasoning LLMs requires the ability to judge the quality of long reasoning traces for effective reasoning data curation, strong training signals during reinforcement lear…
Advancing Creative Physical Intelligence in Large Multimodal Models
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded…
AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions
Ishika Agarwal, Sofia Stoica, Emre Can Acikgoz +4
Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samples. Some works rely on reject…
CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains under…
Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains
Luca Viano, Ruida Zhou, Yifan Sun +4
The class of direct preference optimization (DPO) algorithms has emerged as a promising approach for solving the alignment problem in foundation models. These algorithms work with…
Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs
Xingang Guo, Yaxin Li, Xiangyi Kong +62
Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. Ho…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
Taha Aksu, Devamanyu Hazarika, Shikib Mehri +4
Instruction-based multitasking has played a critical role in the success of large language models (LLMs) in multi-turn dialog applications. While publicly available LLMs have shown…
Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Di Jin, Shikib Mehri, Devamanyu Hazarika +4
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations. Reinforcement learning from human feedback (RLHF)…
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
Chao Zhao, Spandana Gella, Seokhwan Kim +7
Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely…
KILM: Knowledge Injection into Encoder-Decoder Language Models
Yan Xu, Mahdi Namazifar, Devamanyu Hazarika +3
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection in…
Role of Bias Terms in Dot-Product Attention
Mahdi Namazifar, Devamanyu Hazarika, Dilek Hakkani-Tur
Dot-product attention is a core module in the present generation of neural network models, particularly transformers, and is being leveraged across numerous areas such as natural l…
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim +6
This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone do…
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar +3
Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM)…
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar +3
The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks. To address this issue, parameter-e…
Zero-Shot Controlled Generation with Encoder-Decoder Transformers
Devamanyu Hazarika, Mahdi Namazifar, Dilek Hakkani-Tür
Controlling neural network-based models for natural language generation (NLG) has broad applications in numerous areas such as machine translation, document summarization, and dial…
Correcting Automated and Manual Speech Transcription Errors using Warped Language Models
Mahdi Namazifar, John Malik, Li Erran Li +2
Masked language models have revolutionized natural language processing systems in the past few years. A recently introduced generalization of masked language models called warped l…
Language Model is All You Need: Natural Language Understanding as Question Answering
Mahdi Namazifar, Alexandros Papangelis, Gokhan Tur +1
Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning. In this work we study the use of a specific family o…
Warped Language Models for Noise Robust Language Understanding
Mahdi Namazifar, Gokhan Tur, Dilek Hakkani Tür
Masked Language Models (MLM) are self-supervised neural networks trained to fill in the blanks in a given sentence with masked tokens. Despite the tremendous success of MLMs for va…
Joint Contextual Modeling for ASR Correction and Language Understanding
Yue Weng, Sai Sumanth Miryala, Chandra Khatri +8
The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU…
Plato Dialogue System: A Flexible Conversational AI Research Platform
Alexandros Papangelis, Mahdi Namazifar, Chandra Khatri +3
As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the…
Exploration Based Language Learning for Text-Based Games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga +7
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describ…
Flexibly-Structured Model for Task-Oriented Dialogues
Lei Shu, Piero Molino, Mahdi Namazifar +4
This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, wher…
Named Entity Sequence Classification
Mahdi Namazifar
Named Entity Recognition (NER) aims at locating and classifying named entities in text. In some use cases of NER, including cases where detected named entities are used in creating…