6 citations · 13 across the 11 of their papers we have counts for
12 papers
Reinforcement Learning Teachers of Test Time Scaling
Edoardo Cetin, Tianyu Zhao, Yujin Tang
Training reasoning language models (LMs) with reinforcement learning (RL) for one-hot correctness inherently relies on the LM being able to explore and solve its task with some cha…
Sudoku-Bench: Evaluating creative reasoning with Sudoku variants
Jeffrey Seely, Yuki Imajuku, Tianyu Zhao +2
Existing reasoning benchmarks for large language models (LLMs) frequently fail to capture authentic creativity, often rewarding memorization of previously observed patterns. We add…
Large Language Models to Diffusion Finetuning
Edoardo Cetin, Tianyu Zhao, Yujin Tang
We propose a new finetuning method to provide pre-trained large language models (LMs) the ability to scale test-time compute through the diffusion framework. By increasing the numb…
An Evolved Universal Transformer Memory
Edoardo Cetin, Qi Sun, Tianyu Zhao +1
Prior methods propose to offset the escalating costs of modern foundation models by dropping specific parts of their contexts with hand-designed rules, while attempting to preserve…
Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks
Yahui Fu, Haiyue Song, Tianyu Zhao +1
Personality recognition is useful for enhancing robots' ability to tailor user-adaptive responses, thus fostering rich human-robot interactions. One of the challenges in this task…
Focused Prefix Tuning for Controllable Text Generation
Congda Ma, Tianyu Zhao, Makoto Shing +2
In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade the…
End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue
Kentaro Mitsui, Tianyu Zhao, Kei Sawada +3
The recent text-to-speech (TTS) has achieved quality comparable to that of humans; however, its application in spoken dialogue has not been widely studied. This study aims to reali…
Multi-Referenced Training for Dialogue Response Generation
Tianyu Zhao, Tatsuya Kawahara
In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this…
Designing Precise and Robust Dialogue Response Evaluators
Tianyu Zhao, Divesh Lala, Tatsuya Kawahara
Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate co…
End-to-end speech-to-dialog-act recognition
Viet-Trung Dang, Tianyu Zhao, Sei Ueno +2
Spoken language understanding, which extracts intents and/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It…
Effective Incorporation of Speaker Information in Utterance Encoding in Dialog
Tianyu Zhao, Tatsuya Kawahara
In dialog studies, we often encode a dialog using a hierarchical encoder where each utterance is converted into an utterance vector, and then a sequence of utterance vectors is con…
Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation
Tianyu Zhao, Shinsuke Mori, Tatsuya Kawahara
Various encoder-decoder models have been applied to response generation in open-domain dialogs, but a majority of conventional models directly learn a mapping from lexical input to…