7 citations · 23 across the 23 of their papers we have counts for
17 papers · 1 filter
Training Language Models to Critique With Multi-agent Feedback
Tian Lan, Wenwei Zhang, Chengqi Lyu +6
Critique ability, a meta-cognitive capability of humans, presents significant challenges for LLMs to improve. Recent works primarily rely on supervised fine-tuning (SFT) using crit…
InternLM2.5-StepProver: Advancing Automated Theorem Proving via Critic-Guided Search
Zijian Wu, Suozhi Huang, Zhejian Zhou +5
Large Language Models (LLMs) have emerged as powerful tools in mathematical theorem proving, particularly when utilizing formal languages such as LEAN. A prevalent proof method inv…
Scaling Behavior for Large Language Models regarding Numeral Systems: An Example using Pythia
Zhejian Zhou, Jiayu Wang, Dahua Lin +1
Though Large Language Models (LLMs) have shown remarkable abilities in mathematics reasoning, they are still struggling with performing numeric operations accurately, such as addit…
LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover
Zijian Wu, Jiayu Wang, Dahua Lin +1
Recently, large language models have presented promising results in aiding formal mathematical reasoning. However, their performance is restricted due to the scarcity of formal the…
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
Yuzhe Gu, Ziwei Ji, Wenwei Zhang +3
Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications. Current hallucination detection and mitigati…
InternLM-Law: An Open Source Chinese Legal Large Language Model
Zhiwei Fei, Songyang Zhang, Xiaoyu Shen +9
While large language models (LLMs) have showcased impressive capabilities, they struggle with addressing legal queries due to the intricate complexities and specialized expertise r…
Lean Workbook: A large-scale Lean problem set formalized from natural language math problems
Huaiyuan Ying, Zijian Wu, Yihan Geng +3
Large language models have demonstrated impressive capabilities across various natural language processing tasks, especially in solving mathematical problems. However, large langua…
ANAH: Analytical Annotation of Hallucinations in Large Language Models
Ziwei Ji, Yuzhe Gu, Wenwei Zhang +3
Reducing the `' problem of Large Language Models (LLMs) is crucial for their wide applications. A comprehensive and fine-grained measurement of the hallucin…
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
Zifan Song, Yudong Wang, Wenwei Zhang +8
Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typ…
MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark
Hongwei Liu, Zilong Zheng, Yuxuan Qiao +7
Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional p…
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models
Rongjie Li, Songyang Zhang, Dahua Lin +2
Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks. Despite recent advancements, existing methods st…
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks
Chonghua Wang, Haodong Duan, Songyang Zhang +2
Recently, the large language model (LLM) community has shown increasing interest in enhancing LLMs' capability to handle extremely long documents. As various long-text techniques a…
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Xiaoyi Dong, Pan Zhang, Yuhang Zang +21
The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due…
Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
Zehui Chen, Kuikun Liu, Qiuchen Wang +5
Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. How to i…
CriticEval: Evaluating Large Language Model as Critic
Tian Lan, Wenwei Zhang, Chen Xu +4
Critique ability, i.e., the capability of Large Language Models (LLMs) to identify and rectify flaws in responses, is crucial for their applications in self-improvement and scalabl…
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
Huaiyuan Ying, Shuo Zhang, Linyang Li +19
The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which…
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
Zehui Chen, Weihua Du, Wenwei Zhang +8
Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool…