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Found 5 papers, 2 papers with code
Can Large Language Models do Analytical Reasoning?
Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games.
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs.
InFoBench: Evaluating Instruction Following Ability in Large Language Models
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
MeetingBank: A Benchmark Dataset for Meeting Summarization
However, there is a crucial lack of annotated meeting corpora for developing this technology, as it can be hard to collect meetings, especially when the topics discussed are confidential.
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4
Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values.
