8 citations · 19 across the 17 of their papers we have counts for
18 papers
OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning
Ziyou Hu, Zhengliang Shi, Minghang Zhu +5
Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both training and inference. However, exist…
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
Minghang Zhu, Zhengliang Shi, Zhiwei Xu +5
The advancement of large language models (LLMs) has enabled the construction of multi-agent systems to solve complex tasks by dividing responsibilities among specialized agents, su…
Evolution without Large Models: Training Language Model with Task Principles
Minghang Zhu, Shen Gao, Zhengliang Shi +5
A common training approach for language models involves using a large-scale language model to expand a human-provided dataset, which is subsequently used for model training.This me…
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization
Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang +5
Large Visual Language Models (LVLMs) have demonstrated impressive capabilities across multiple tasks. However, their trustworthiness is often challenged by hallucinations, which ca…
Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
Zhengliang Shi, Lingyong Yan, Dawei Yin +3
Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques. However, effectively enabling LLMs to seek accurate knowledge…
TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation
Chengrui Huang, Shen Gao, Zhengliang Shi +2
Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in prefer…
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
Zhengliang Shi, Lingyong Yan, Weiwei Sun +7
Retrieval-augmented generation (RAG) integrates large language models ( LLM s) with retrievers to access external knowledge, improving the factuality of LLM generation in knowledge…
Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models
Zhengliang Shi, Yuhan Wang, Lingyong Yan +4
Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool…
Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation
Dongsheng Zhu, Weixian Shi, Zhengliang Shi +4
Although current Large Language Models (LLMs) exhibit impressive capabilities, performing complex real-world tasks still requires tool learning. Mainstream methods, such as CoT/ReA…
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Weiwei Sun, Zhengliang Shi, Jiulong Wu +6
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially…
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Chengrui Huang, Zhengliang Shi, Yuntao Wen +4
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to en…
Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering
Zhengliang Shi, Weiwei Sun, Shen Gao +3
Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large language models (LLMs) due to the intensive knowledge required. Current solutions, like Retrieva…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
360REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
Shen Gao, Hao Li, Chengrui Huang +4
Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to…
Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi, Shen Gao, Xiuyi Chen +7
Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively sele…
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue
Zhengliang Shi, Weiwei Sun, Shuo Zhang +3
Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now,…
Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
Shen Gao, Zhengliang Shi, Minghang Zhu +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs. Although some works employ open-source LLMs for…
Contrastive Learning Reduces Hallucination in Conversations
Weiwei Sun, Zhengliang Shi, Shen Gao +3
Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the probl…