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The Sufficiency-Conciseness Trade-off in LLM Self-Explanation from an Information Bottleneck Perspective

Ali Zahedzadeh, Behnam Bahrak · Feb 15, 2026 · Citations: 0

Automatic Metrics Long Horizon Multilingual
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

Background context only

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

Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering. While these explanations enhance accuracy, they are often verbose and costly to generate, raising the question of how much explanation is truly necessary. In this paper, we examine the trade-off between sufficiency, defined as the ability of an explanation to justify the correct answer, and conciseness, defined as the reduction in explanation length. Building on the information bottleneck principle, we conceptualize explanations as compressed representations that retain only the information essential for producing correct answers.To operationalize this view, we introduce an evaluation pipeline that constrains explanation length and assesses sufficiency using multiple language models on the ARC Challenge dataset. To broaden the scope, we conduct experiments in both English, using the original dataset, and Persian, as a resource-limited language through translation. Our experiments show that more concise explanations often remain sufficient, preserving accuracy while substantially reducing explanation length, whereas excessive compression leads to performance degradation.

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.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

Main weakness

No major weakness surfaced.

Trust level

Moderate

Usefulness score

25/100 • Low

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

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent 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

missing

None explicit

No explicit feedback protocol extracted.

"Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering."

Benchmarks / Datasets

strong

ARC Challenge

Useful for quick benchmark comparison.

"Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering."

Reported Metrics

strong

Accuracy, Conciseness

Useful for evaluation criteria comparison.

"While these explanations enhance accuracy, they are often verbose and costly to generate, raising the question of how much explanation is truly necessary."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: Multilingual

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: Long Horizon
  • Quality controls: Not reported
  • Evidence quality: Moderate
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

ARC-Challenge

Reported Metrics

accuracyconciseness

Research Brief

Metadata summary

Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering.

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

Key Takeaways

  • Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering.
  • While these explanations enhance accuracy, they are often verbose and costly to generate, raising the question of how much explanation is truly necessary.
  • In this paper, we examine the trade-off between sufficiency, defined as the ability of an explanation to justify the correct answer, and conciseness, defined as the reduction in explanation length.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • 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

Accuracy

Research Summary

Contribution Summary

  • While these explanations enhance accuracy, they are often verbose and costly to generate, raising the question of how much explanation is truly necessary.
  • Building on the information bottleneck principle, we conceptualize explanations as compressed representations that retain only the information essential for producing correct answers.To operationalize this view, we introduce an evaluation…
  • Our experiments show that more concise explanations often remain sufficient, preserving accuracy while substantially reducing explanation length, whereas excessive compression leads to performance degradation.

Why It Matters For Eval

  • Building on the information bottleneck principle, we conceptualize explanations as compressed representations that retain only the information essential for producing correct answers.To operationalize this view, we introduce an evaluation…

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: ARC-Challenge

  • Pass: Metric reporting is present

    Detected: accuracy, conciseness

Related Papers

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

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