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Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models

Tobias Schreieder, Tim Schopf, Michael Färber · Aug 21, 2025 · Citations: 0

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How to use this page

Low trust

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

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness. As a result, a growing body of research focuses on evidence-based text generation with LLMs, aiming to link model outputs to supporting evidence to ensure traceability and verifiability. However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks. To bridge this gap, we systematically analyze 134 papers, introduce a unified taxonomy of evidence-based text generation with LLMs, and investigate 300 evaluation metrics across seven key dimensions. Thereby, we focus on approaches that use citations, attribution, or quotations for evidence-based text generation. Building on this, we examine the distinctive characteristics and representative methods in the field. Finally, we highlight open challenges and outline promising directions for future work.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

This paper looks adjacent to evaluation work, but not like a strong protocol reference.
The available metadata is too thin to trust this as a primary source.
The abstract does not clearly describe the evaluation setup.
The abstract does not clearly name benchmarks or metrics.

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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

Background context only.

Main weakness

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

Trust level

Low

Usefulness score

0/100 • Low

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

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

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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.

"The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness."

Quality Controls

missing

Not reported

No explicit QC controls found.

"The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness."

Human Feedback Details

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

Evaluation Details

Evaluation modes:
Agentic eval: None
Quality controls: Not reported
Evidence quality: Low
Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

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

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness.

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

Key Takeaways

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness.
As a result, a growing body of research focuses on evidence-based text generation with LLMs, aiming to link model outputs to supporting evidence to ensure traceability and verifiability.
However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks.

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

Research Summary

Contribution Summary

However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks.
To bridge this gap, we systematically analyze 134 papers, introduce a unified taxonomy of evidence-based text generation with LLMs, and investigate 300 evaluation metrics across seven key dimensions.

Why It Matters For Eval

However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks.
To bridge this gap, we systematically analyze 134 papers, introduce a unified taxonomy of evidence-based text generation with LLMs, and investigate 300 evaluation metrics across seven key dimensions.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Gap: Evaluation mode is explicit

No clear evaluation mode extracted.
Gap: Quality control reporting appears

No calibration/adjudication/IAA control explicitly detected.
Gap: Benchmark or dataset anchors are present

No benchmark/dataset anchor extracted from abstract.
Gap: Metric reporting is present

No metric terms extracted.

Related Papers

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