10 citations · 19 across the 17 of their papers we have counts for
18 papers
AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment
Zongqian Li, Yaoyiran Li, Yaohui Guo +3
Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually ends at alpha generation whil…
Flexi-LoRA with Input-Adaptive Ranks: Efficient Finetuning for Speech and Reasoning Tasks
Zongqian Li, Yixuan Su, Han Zhou +2
Parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) have become essential for deploying large language models, yet their static parameter allocation remains sub…
Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
Zongqian Li, Tengchao Lv, Shaohan Huang +8
Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We…
Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models
Zongqian Li, Shaohan Huang, Zewen Chi +5
Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and da…
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning
Zheng Hui, Yijiang River Dong, Sanhanat Sivapiromrat +2
When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send the query to a powerful proprietary LLM…
A Survey on Prompt Tuning
Zongqian Li, Yixuan Su, Nigel Collier
This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classif…
TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law
Zheng Hui, Yijiang River Dong, Ehsan Shareghi +1
As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compli…
PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning
Zongqian Li, Yixuan Su, Nigel Collier
Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting large language models, yet existing approaches exhibit counter-intuitive phenomena: integrating router…
ReasonGraph: Visualisation of Reasoning Paths
Zongqian Li, Ehsan Shareghi, Nigel Collier
Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools. We present ReasonGraph, a web-bas…
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…
500xCompressor: Generalized Prompt Compression for Large Language Models
Zongqian Li, Yixuan Su, Nigel Collier
Prompt compression is crucial for enhancing inference speed, reducing costs, and improving user experience. However, current methods face challenges such as low compression ratios…
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…
POSQA: Probe the World Models of LLMs with Size Comparisons
Chang Shu, Jiuzhou Han, Fangyu Liu +2
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and…
Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective
Huayang Li, Tian Lan, Zihao Fu +5
There are a number of diverging hypotheses about the neural text degeneration problem, i.e., generating repetitive and dull loops, which makes this problem both interesting and con…
FireAct: Toward Language Agent Fine-tuning
Baian Chen, Chang Shu, Ehsan Shareghi +3
Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act. However, most of the…