aiqbench analysis
The paper proposes COMPREHEND, a method for automatically building fine-grained hierarchies of user prompts, with each category described by a sentence rather than a broad cluster label. On WildBench it reports large gains over EvalTree on entailment/faithfulness and informativeness metrics, though these are self-reported results on one benchmark. For LLM evaluators, the practical question is whether finer-grained weakness reports give more actionable guidance than coarse taxonomies, and independent replication across other benchmarks will be needed to confirm that.
COMPREHEND: Ultra-Fine-Grained Benchmark Understanding by Entailment Hierarchy
arXiv:2611.04249v1 Announce Type: new Abstract: Understanding what kind of prompts users typically ask and how well an LLM performs on these prompt categories is crucial for LLM practitioners. The existing methods satisfy the demand by hierarchically clustering the user prompts and synthesizing the cluster names using an LLM to build a taxonomy. However, the cluster names from these methods are usually too general for humans to understand the detailed prompt distributions and identify the exact strengths and weaknesses of LLMs. In this study, we propose COMPREHEND, which automatically constructs an ultra-fine-grained entailment hierarchy for the prompts. The entailment hierarchy replaces the cluster names in the taxonomy with sentences to provide more precise cluster descriptions for more fine-grained categorization. Compared with EvalTree, a state-of-the-art taxonomy construction method, COMPREHEND improves an entailment/faithfulness metric from 22.6 to 41.7 and an informative metric from 16.3 to 39.1 in WildBench. Our experiments also show that the weakness report generated based on our entailment hierarchy is much more informative than EvalTree in terms of predicting the response scores in the benchmark. All the code is released in https://github.com/JackNapier20/COMPREHEND_public.
Abstract:Understanding what kind of prompts users typically ask and how well an LLM performs on these prompt categories is crucial for LLM practitioners. The existing methods satisfy the demand by hierarchically clustering the user prompts and synthesizing the cluster names using an LLM to build a taxonomy. However, the cluster names from these methods are usually too general for humans to understand the detailed prompt distributions and identify the exact strengths and weaknesses of LLMs. In this study, we propose COMPREHEND, which automatically constructs an ultra-fine-grained entailment hierarchy for the prompts. The entailment hierarchy replaces the cluster names in the taxonomy with sentences to provide more precise cluster descriptions for more fine-grained categorization. Compared with EvalTree, a state-of-the-art taxonomy construction method, COMPREHEND improves an entailment/faithfulness metric from 22.6 to 41.7 and an informative metric from 16.3 to 39.1 in WildBench. Our experiments also show that the weakness report generated based on our entailment hierarchy is much more informative than EvalTree in terms of predicting the response scores in the benchmark. All the code is released in this https URL.
Computation and Language (cs.CL)
arXiv:2611.04249 [cs.CL]
(or arXiv:2611.04249v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.04249 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 274 words