Papers by Kevin Yang
PREADD: Prefix-Adaptive Decoding for Controlled Text Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for controlling text generation rely on auxiliary expert models but require training data. |
| Approach: | They propose a flexible method for controlled text generation that uses a prefix-prepended prompt instead of an external model. |
| Outcome: | The proposed method outperforms auxiliary-expert methods on three tasks by 12% or more on main metrics for each task. |
Learning Personalized Alignment for Evaluating Open-ended Text Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Traditional evaluation metrics rely heavily on lexical similarity with human-written references, showing poor correlation with human judgments and failing to account for alignment with the diversity of human preferences. |
| Approach: | They propose an interpretable evaluation framework that evaluates alignment with specific human preferences by providing detailed comments and fine-grained scoring. |
| Outcome: | The proposed framework outperforms GPT-4 in Kendall correlation and accuracy with zero-shot reviewers. |
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement (2025.coling-main)
Copied to clipboard
Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, Heng Ji
| Challenge: | Existing research has focused on enhancing the retrieval stage and optimizing the representation of the database. |
| Approach: | They propose a framework to improve generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
| Outcome: | The proposed framework improves generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
Modular Visual Question Answering via Code Generation (2023.acl-short)
Copied to clipboard
Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar, Kevin Yang, Arsha Nagrani, Cordelia Schmid, Andy Zeng, Trevor Darrell, Dan Klein
| Challenge: | a framework for visual question answering is based on modular code generation . the scope of reasoning needed for visual questions is vast, and requires many skills . |
| Approach: | They propose a framework that formulates visual question answering as modular code generation. |
| Outcome: | The proposed framework improves accuracy on COVR and GQA datasets by 3% and 2% compared to the few-shot baseline that does not employ code generation. |
ThoughtSculpt: Reasoning with Intermediate Revision and Search (2025.findings-naacl)
Copied to clipboard
| Challenge: | THOUGHTSCULPT is a general reasoning and search method for tasks with outputs that can be decomposed into components. |
| Approach: | They propose a general reasoning and search method for tasks with outputs that can be decomposed into components. |
| Outcome: | THOUGHTSCULPT outperforms state-of-the-art reasoning methods on three tasks . authors show that distinct prompting strategies can influence the performance of LLMs . |
Improving Pacing in Long-Form Story Planning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing systems for writing long-form stories suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details. |
| Approach: | They propose a system that uses a concreteness evaluator to judge which of two events is more concrete. |
| Outcome: | The proposed system improves pacing when automatically generating story outlines. |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
Copied to clipboard
Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |
FactTrack: Time-Aware World State Tracking in Story Outlines (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing language models still struggle to reason over long context windows . et al., 2022, show that long context generation is a challenge for LLMs . |
| Approach: | They propose a method for tracking atomic facts and addressing factual contradictions . they use a four-step pipeline to update a world state data structure for each new event . |
| Outcome: | The proposed method outperforms a baseline and fair method on story outlines. |
WildSci: Advancing Scientific Reasoning from In-the-Wild Literature (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language model reasoning focus on mathematics and coding domains, but scientific reasoning remains limited in other domains due to limited dataset coverage. |
| Approach: | They propose a framework for sustainable scientific reasoning QA generation by synthesizing a new dataset of domain-specific science questions from peer-reviewed literature. |
| Outcome: | The proposed framework and dataset enable scalable and sustainable research in scientific reasoning. |
A Streaming Approach For Efficient Batched Beam Search (2020.emnlp-main)
Copied to clipboard
| Challenge: | During decoding, candidates terminate or are pruned according to heuristics, a streaming method is used to "refill" the batch after it finishes translating some fraction of the current batch. |
| Approach: | They propose an efficient batching strategy for variable-length decoding on GPU architectures by streamlining the batching process. |
| Outcome: | The proposed method reduces runtime by 71% compared to a fixed-width beam search baseline and 17% compared with a variable-widness baseline while matching baselines’ BLEU. |
Automated Crossword Solving (2022.acl-long)
Copied to clipboard
| Challenge: | Using neural question answering models, our system generates answer candidates and then combines loopy belief propagation with local search to find full puzzle solutions. |
| Approach: | They propose a new approach to automatically solving crossword puzzles that uses neural question answering models and loopy belief propagation with local search to find full puzzle solutions. |
| Outcome: | The proposed system outperforms even the best human solvers and can solve crosswords from a wide range of domains with perfect accuracy. |
Audio-Aware Large Language Models as Judges for Speaking Styles (2025.findings-emnlp)
Copied to clipboard
Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin, Kevin Lin, Linjie Li, Radu Kopetz, Yao Qian, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang
| Challenge: | Audio-aware large language models (ALLMs) can understand textual and non-textual information in the audio input. |
| Approach: | They use audio-aware large language models (ALLMs) to evaluate the speaking styles of SLMs on two tasks: voice style instruction following and role-playing. |
| Outcome: | The proposed models can understand the textual and non-textual information in the audio input and can be used as a judge to assess the speaking styles of SLMs. |
FUDGE: Controlled Text Generation With Future Discriminators (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent advances in large pretrained language models allow us to generate increasingly realistic text by modeling a distribution P (X) over natural language sequences X. |
| Approach: | They propose a flexible and modular method for controlled text generation that uses a Bayesian decomposition of the conditional distribution of G given an attribute predictor and can easily compose predictors for multiple desired attributes. |
| Outcome: | The proposed method can be easily composed and performs three tasks. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
Copied to clipboard
Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Gunrock: A Social Bot for Complex and Engaging Long Conversations (D19-3)
Copied to clipboard
Dian Yu, Michelle Cohn, Yi Mang Yang, Chun Yen Chen, Weiming Wen, Jiaping Zhang, Mingyang Zhou, Kevin Jesse, Austin Chau, Antara Bhowmick, Shreenath Iyer, Giritheja Sreenivasulu, Sam Davidson, Ashwin Bhandare, Zhou Yu
| Challenge: | Gunrock is a speech-based social chatbot that can be used to understand complex sentences and have in-depth conversations. |
| Approach: | They propose a system that allows users to understand complex sentences and have in-depth conversations in open domains. |
| Outcome: | The proposed system produces longer sentences, which are directly related to user engagement (e.g., ratings, number of turns). |
PoMo: Generating Entity-Specific Post-Modifiers in Context (N19-1)
Copied to clipboard
Jun Seok Kang, Robert Logan, Zewei Chu, Yang Chen, Dheeru Dua, Kevin Gimpel, Sameer Singh, Niranjan Balasubramanian
| Challenge: | Using crowdsourcing, we show that contextual relevance is necessary for accurate post-modifier generation. |
| Approach: | They introduce entity post-modifier generation as an instance of a collaborative writing task . they build a post- modifier dataset from news articles that provides contextually relevant information about the target entity. |
| Outcome: | The proposed system can generate a post-modifier phrase that provides contextually relevant information about the target entity. |
Re3: Generating Longer Stories With Recursive Reprompting and Revision (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has generated short stories of several pages in length, but they are much shorter than typical short stories meant for human consumption. |
| Approach: | They propose a framework to generate long-range plot coherence and relevance by prompting a general-purpose language model and a language model. |
| Outcome: | The proposed framework generates stories of 2000-2500 words, compared to similar-length stories generated directly from the same model. |
DOC: Improving Long Story Coherence With Detailed Outline Control (2023.acl-long)
Copied to clipboard
| Challenge: | Detailed Outline Control (DOC) framework improves long-range plot coherence . human evaluations of DOC show it outperforms strong Re3 on plot cohesion, outline relevance and interestingness . |
| Approach: | They propose a Detailed Outline Control framework to improve long-range plot coherence . the detailed outliner creates a more detailed, hierarchically structured outline . they propose doc with a detailed controller to ensure the more detailed outline is respected . |
| Outcome: | The proposed framework outperforms Re3 on plot coherence, outline relevance and interestingness. |
Shanks: Simultaneous Hearing and Thinking for Spoken Language Models (2026.acl-long)
Copied to clipboard
Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin, Kevin Lin, Shujie Liu, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang
| Challenge: | Existing large language models and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. |
| Approach: | They propose a general inference framework that enables SLMs to generate unspoken chain-of-thought reasoning while listening to user input. |
| Outcome: | The proposed framework enhances real-time user–SLM interaction in two scenarios. |
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation (2022.findings-acl)
Copied to clipboard
| Challenge: | a low-resource task-oriented semantic parser is limited by privacy requirements for unlabeled natural utterances. |
| Approach: | They propose a setup for low-resource task-oriented semantic parsing based on user interactions . they use structured canonical utterances, then simulating corresponding natural language to improve performance. |
| Outcome: | The proposed setup improves on a low-resource task-oriented semantic parser using utterances collected through user interactions. |
EntEval: A Holistic Evaluation Benchmark for Entity Representations (D19-1)
Copied to clipboard
| Challenge: | EntEval is a test suite of tasks that require nontrivial understanding of entities. |
| Approach: | They propose to encode the mention context or the Wikipedia hyperlink annotations to learn better entity representations. |
| Outcome: | The proposed model improves strong baselines on multiple EntEval tasks. |