Pith. sign in

REVIEW 1 cited by

Language Models as a Service: Overview of a New Paradigm and its Challenges

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.16573 v2 pith:VWK3CA33 submitted 2023-09-28 cs.AI cs.CLcs.CY

classification cs.AIcs.CLcs.CY
keywords modelslanguagechallengeslmaasexistinghandinterfacesoverview
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or software programming interfaces. This is the Language-Models-as-a-Service (LMaaS) paradigm. In contrast with scenarios where full model access is available, as in the case of open-source models, such closed-off language models present specific challenges for evaluating, benchmarking, and testing them. This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LMaaS. We systematically examine the issues that arise from a lack of information about language models for each of these four aspects. We conduct a detailed analysis of existing solutions and put forth a number of considered recommendations, and highlight the directions for future advancements. On the other hand, it serves as a comprehensive resource for existing knowledge on current, major LMaaS, offering a synthesized overview of the licences and capabilities their interfaces offer.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Code Simulation as a Proxy for High-order Tasks in Large Language Models

    cs.LG 2025-02 conditional novelty 5.0 of 10

    LLM performance on naturalistic reasoning tasks tracks performance on equivalent Python code simulation, but the effect is partly driven by pattern matching and memorization rather than faithful execution.

Pith tools