activity
20232026
most citedThe Rise and Potential of Large Language Model Based Agents: A Survey

256 citations · 279 across the 26 of their papers we have counts for

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13 papers · 1 filter

cs.CL2026

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment

Yuming Yang, Mingyoung Lai, Wanxu Zhao +13

Long chain-of-thought (CoT) trajectories provide rich supervision signals for distilling reasoning from teacher to student LLMs. However, both prior work and our experiments show t…

cs.CL2025

Critique-RL: Training Language Models for Critiquing through Two-Stage Reinforcement Learning

Zhiheng Xi, Jixuan Huang, Xin Guo +15

Training critiquing language models to assess and provide feedback on model outputs is a promising way to improve LLMs for complex reasoning tasks. However, existing approaches typ…

cs.CL2025

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

Junjie Ye, Changhao Jiang, Zhengyin Du +8

Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of efficient reinforcement learning (R…

cs.CL2025

SpeechRole: A Large-Scale Dataset and Benchmark for Evaluating Speech Role-Playing Agents

Changhao Jiang, Jiajun Sun, Yifei Cao +15

Speech is essential for realistic role-playing, yet existing work on role-playing agents largely centers on text, leaving Speech Role-Playing Agents (SRPAs) underexplored and witho…

cs.CL2025

BMMR: A Large-Scale Bilingual Multimodal Multi-Discipline Reasoning Dataset

Zhiheng Xi, Guanyu Li, Yutao Fan +13

In this paper, we introduce BMMR, a large-scale bilingual, multimodal, multi-disciplinary reasoning dataset for the community to develop and evaluate large multimodal models (LMMs)…

cs.CL2025

Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective

Zhihao Zhang, Qiaole Dong, Qi Zhang +12

Post-training algorithms such as Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) are widely used to adapt (multimodal) large language models to downstream tasks. W…

cs.CL2025

What Makes a Good Speech Tokenizer for LLM-Centric Speech Generation? A Systematic Study

Xiaoran Fan, Zhichao Sun, Yangfan Gao +19

Speech-language models (SLMs) offer a promising path toward unifying speech and text understanding and generation. However, challenges remain in achieving effective cross-modal ali…

cs.CL2025

Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training

Changhao Jiang, Ming Zhang, Yifei Cao +12

The GPT-4 technical report suggests that downstream performance can be predicted from pre-training signals, but offers little methodological detail on how to quantify this. This wo…

cs.CL2024

Distill Visual Chart Reasoning Ability from LLMs to MLLMs

Wei He, Zhiheng Xi, Wanxu Zhao +6

Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs), including recognizing key information from visual inputs a…

cs.CL2024

RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning

Junjie Ye, Yilong Wu, Songyang Gao +7

Tool learning has generated widespread interest as a vital means of interaction between Large Language Models (LLMs) and the physical world. Current research predominantly emphasiz…

cs.CL2024★ 1 cited

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios

Junjie Ye, Guanyu Li, Songyang Gao +9

Existing evaluations of tool learning primarily focus on validating the alignment of selected tools for large language models (LLMs) with expected outcomes. However, these approach…

cs.CL2023★ 8 cited

LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Shihan Dou, Enyu Zhou, Yan Liu +13

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.…

cs.CL2023★ 1 cited

RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms

Enyu Zhou, Rui Zheng, Zhiheng Xi +7

Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors. However, current research ten…