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A survey on evaluation of large language models
Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …
industry, owing to their unprecedented performance in various applications. As LLMs …
Simpo: Simple preference optimization with a reference-free reward
Abstract Direct Preference Optimization (DPO) is a widely used offline preference
optimization algorithm that reparameterizes reward functions in reinforcement learning from …
optimization algorithm that reparameterizes reward functions in reinforcement learning from …
Chatbot arena: An open platform for evaluating llms by human preference
Large Language Models (LLMs) have unlocked new capabilities and applications; however,
evaluating the alignment with human preferences still poses significant challenges. To …
evaluating the alignment with human preferences still poses significant challenges. To …
Self-play fine-tuning converts weak language models to strong language models
Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …
Benchmarking large language models in retrieval-augmented generation
Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the
hallucination of large language models (LLMs). However, existing research lacks rigorous …
hallucination of large language models (LLMs). However, existing research lacks rigorous …
Toolllm: Facilitating large language models to master 16000+ real-world apis
Despite the advancements of open-source large language models (LLMs), eg, LLaMA, they
remain significantly limited in tool-use capabilities, ie, using external tools (APIs) to fulfill …
remain significantly limited in tool-use capabilities, ie, using external tools (APIs) to fulfill …
H2o: Heavy-hitter oracle for efficient generative inference of large language models
Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
are notably cost-prohibitive to deploy, particularly for applications involving long-content …