6 papers · 1 filter
CFBench: A Comprehensive Constraints-Following Benchmark for LLMs
Tao Zhang, Chenglin Zhu, Yanjun Shen +10
The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications…
Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
Mingyang Chen, Haoze Sun, Tianpeng Li +7
Large Language Models (LLMs) have exhibited significant potential in performing diverse tasks, including the ability to call functions or use external tools to enhance their perfor…
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification
Linzhuang Sun, Hao Liang, Jingxuan Wei +5
According to the Test-Time Scaling, the integration of External Slow-Thinking with the Verify mechanism has been demonstrated to enhance multi-round reasoning in large language mod…
FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback
Youquan Li, Miao Zheng, Fan Yang +5
Human feedback is crucial in the interactions between humans and Large Language Models (LLMs). However, existing research primarily focuses on benchmarking LLMs in single-turn dial…
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
Guosheng Dong, Da Pan, Yiding Sun +17
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several ins…
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
Miao Zheng, Hao Liang, Fan Yang +16
In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out…