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HFEPX · Eval paper review

Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations

Noah Y. Siegel, Nicolas Heess, Maria Perez-Ortiz, Oana-Maria Camburu

Published

Mar 17, 2025

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

35% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jul 1, 2026

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Automatic Metrics

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

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

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
0/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

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Abstract

When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans. But are these explanations faithful, i.e. do they convey the factors actually responsible for the decision? In this work, we analyse counterfactual faithfulness across 75 models from 13 families. We analyze the tradeoff between conciseness and comprehensiveness, how correlational faithfulness metrics assess this tradeoff, and the extent to which metrics can be gamed. This analysis motivates two new metrics: the phi-CCT, a simplified variant of the Correlational Counterfactual Test (CCT) which avoids the need for token probabilities while explaining most of the variance of the original test; and F-AUROC, which eliminates sensitivity to imbalanced intervention distributions and captures a model's ability to produce explanations with different levels of detail. Our findings reveal a clear scaling trend: larger and more capable models are consistently more faithful on all metrics we consider. Our code is available at https://github.com/google-deepmind/corr_faith.

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

missing

None explicit

No explicit feedback protocol extracted.

"When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans."

Quality Controls

missing

Not reported

No explicit QC controls found.

"When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans."

Reported Metrics

partial

Auroc, Faithfulness, Conciseness

Useful for evaluation criteria comparison.

"In this work, we analyse counterfactual faithfulness across 75 models from 13 families."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

aurocfaithfulnessconciseness
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Coding
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.

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

Key takeaways

  • When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.
  • But are these explanations faithful, i.e.
  • do they convey the factors actually responsible for the decision?

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.

Recommended queries

Contribution summary

  • When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.

Why it matters for eval

  • When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: auroc, faithfulness, conciseness