8 citations · 14 across the 9 of their papers we have counts for
10 papers
When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?
Xinzhe Li, Yaguang Tao
Multi-trajectory inference for tool-use LLM agents - generating multiple reasoning attempts and selecting among them - benefits from transferring knowledge across attempts so that…
LiTS: A Modular Framework for LLM Tree Search
Xinzhe Li, Yaguang Tao
LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition, and RewardModel) that plug into…
Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search
Xinzhe Li
Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via tree search (LITS) achieves stron…
A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks
Xinzhe Li
LLM test-time compute (or LLM inference) via search has emerged as a promising research area with rapid developments. However, current frameworks often adopt distinct perspectives…
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning
Xinzhe Li
Tool use, planning, and feedback learning are currently three prominent paradigms for developing Large Language Model (LLM)-based agents across various tasks. Although numerous fra…
Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting
Xinzhe Li, Ming Liu
Over the last decade, a wide range of training and deployment strategies for Large Language Models (LLMs) have emerged. Among these, the prompting paradigms of Auto-regressive LLMs…
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language Model
Xinzhe Li, Ming Liu, Shang Gao
Retrieval-Augmented Generation (RAG) systems are widely used across various industries for querying closed-domain and in-house knowledge bases. However, evaluating these systems pr…
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
Xinzhe Li, Ming Liu, Shang Gao
This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution. Specifically, building on the work…
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?
Xinzhe Li, Ming Liu, Shang Gao
For Pretrained Language Models (PLMs), their susceptibility to noise has recently been linked to subword segmentation. However, it is unclear which aspects of segmentation affect t…
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
Xinzhe Li, Ming Liu, Shang Gao +1
Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. Howev…