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Found 7 papers, 6 papers with code
AIGS: Generating Science from AI-Powered Automated Falsification
By revisiting the definition of scientific research, we argue that $\textit{falsification}$ is the essence of both human research process and the design of an AIGS system.
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination.
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models
State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional capabilities in vision-language tasks.
AlignBench: Benchmarking Chinese Alignment of Large Language Models
Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants.
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation
Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing works train a critique generation model on the evaluation data labeled by GPT-4's direct prompting.
SafetyBench: Evaluating the Safety of Large Language Models
Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages.
AgentBench: Evaluating LLMs as Agents
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
