Skip to content
← Back to explorer

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench

David Schlangen, Sherzod Hakimov, Chalamalasetti Kranti, Jonathan Jordan, Philipp Sadler · Jul 11, 2025 · Citations: 0

General Pairwise Preference
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Moderate trust

Use this for comparison and orientation, not as your only source.

Best use

Secondary protocol comparison source

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Moderate

Derived from extracted protocol signals and abstract evidence.

Abstract

There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation. The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available. The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose responses the user then selects their most preferred one. The former paradigm hence excels at control over what is tested, while the latter comes with higher ecological validity, testing actual use cases interactively. Recently, a third complementary paradigm has emerged that combines some of the strengths of these approaches, offering control over multi-turn, reference-free, repeatable interactions, while stressing goal-directedness: dialogue game based evaluation. While the utility of this approach has been shown by several projects, its adoption has been held back by the lack of a mature, easily re-usable implementation. In this paper, we present clembench, which has been in continuous development since 2023 and has in its latest release been optimized for ease of general use. We describe how it can be used to benchmark one's own models (using a provided set of benchmark game instances in English), as well as how easily the benchmark itself can be extended with new, tailor-made targeted tests.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • The abstract does not clearly describe the evaluation setup.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Best use

Secondary protocol comparison source

Use if you need

Background context only.

Main weakness

The abstract does not clearly describe the evaluation setup.

Trust level

Moderate

Usefulness score

50/100 • Medium

Useful as a secondary reference; validate protocol details against neighboring papers.

Human Feedback Signal

Detected

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Moderate-confidence candidate

Extraction confidence 55%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Pairwise Preference

Directly usable for protocol triage.

"There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation."

Quality Controls

missing

Not reported

No explicit QC controls found.

"There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation."

Benchmarks / Datasets

strong

LMSYS Chatbot Arena, Clembench, Lm Arena

Useful for quick benchmark comparison.

"The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose responses the user then selects their most preferred one."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation."

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Pairwise Preference
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Moderate
  • Use this page as: Secondary protocol comparison source

Protocol And Measurement Signals

Benchmarks / Datasets

LMSYS Chatbot ArenaClembenchLm-Arena

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation.
  • The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available.
  • The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose responses the user then selects their most preferred one.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation.
  • The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available.
  • In this paper, we present clembench, which has been in continuous development since 2023 and has in its latest release been optimized for ease of general use.

Why It Matters For Eval

  • There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation.
  • The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: LMSYS Chatbot Arena, Clembench, Lm-Arena

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Create Account & Post a Job
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
Learn About Managed Service
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.
Join Now