10 citations · 13 across the 14 of their papers we have counts for
15 papers
Trajectory-Level Speculation with Semantic Verification for Tool-Using Agents
Zilin Wang, Yinhong Liu, Chao Chen +2
Large language model agents solve tasks through long sequences of tool calls, and the prevailing design has a single strong model generate every step, although many steps are routi…
From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning
Chao Chen, Chengzu Li, Zhiwei Li +2
Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically inf…
Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking
Songbo Hu, Yinhong Liu, Ej Zhou +5
Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-parallel datasets at scale. This…
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
Minrui Xu, Zilin Wang, Mengyi DENG +12
Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and…
When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
Yijiang River Dong, Tiancheng Hu, Yinhong Liu +2
While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences ac…
Prompt Compression for Large Language Models: A Survey
Zongqian Li, Yinhong Liu, Yixuan Su +1
Leveraging large language models (LLMs) for complex natural language tasks typically requires long-form prompts to convey detailed requirements and information, which results in in…
Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models
Yinhong Liu, Zhijiang Guo, Tianya Liang +3
Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs often show inconsistencies in their judgme…
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Zhongshen Zeng, Yinhong Liu, Yingjia Wan +16
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. Howeve…
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
Han Zhou, Xingchen Wan, Yinhong Liu +3
Large language models (LLMs) have shown promising abilities as cost-effective and reference-free evaluators for assessing language generation quality. In particular, pairwise LLM e…
Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks. When it comes to long-form text generati…
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
Yinhong Liu, Han Zhou, Zhijiang Guo +4
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language. However, LLMs still exhibit bi…
TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles
Yinhong Liu, Yimai Fang, David Vandyke +1
In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across div…
Instruct-SCTG: Guiding Sequential Controlled Text Generation through Instructions
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Instruction-tuned large language models have shown remarkable performance in aligning generated text with user intentions across various tasks. However, maintaining human-like disc…
Plug-and-Play Recipe Generation with Content Planning
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Recent pre-trained language models have shown promising capabilities in generating fluent and realistic natural language text. However, generating multi-sentence text with global c…
Learning Functional Distributional Semantics with Visual Data
Yinhong Liu, Guy Emerson
Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a wor…