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HFEPX · Eval paper review

Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering

Julius Gun, Timo Oksanen

Published

Aug 25, 2025

Citations

0

Trust level

Moderate

Usefulness score

37/100 (Low)

Extraction confidence

55% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Mar 6, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics Llm As Judge Multilingual

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 for comparison and orientation, not as your only source.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

What to verify

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

Main weakness

No major weakness surfaced.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
37/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task. Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German. It simulates a cross-lingual information retrieval scenario where questions are posed in English against all three language versions of the manual. The evaluation focuses on realistic "needle-in-a-haystack" challenges and includes unanswerable questions to test for hallucinations. We compare nine long-context LLMs using direct prompting against three Retrieval-Augmented Generation (RAG) strategies (keyword, semantic, hybrid), with an LLM-as-a-judge for evaluation. Our findings for this specific manual show that Hybrid RAG consistently outperforms direct long-context prompting. Models like Gemini 2.5 Flash and the smaller Qwen 2.5 7B achieve high accuracy (over 85%) across all languages with RAG. This paper contributes a detailed analysis of LLM performance in a specialized industrial domain and an open framework for similar evaluations, highlighting practical trade-offs and challenges.

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.

"We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task."

Evaluation Modes

strong

Llm As Judge, Automatic Metrics

Includes extracted eval setup.

"We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task."

Quality Controls

missing

Not reported

No explicit QC controls found.

"We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task."

Benchmarks / Datasets

strong

Needle In A Haystack

Useful for quick benchmark comparison.

"We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task."

Reported Metrics

strong

Accuracy

Useful for evaluation criteria comparison.

"Models like Gemini 2.5 Flash and the smaller Qwen 2.5 7B achieve high accuracy (over 85%) across all languages with RAG."

Benchmarks and datasets

Needle In A Haystack

Reported metrics

accuracy
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Multilingual
Evaluation details
Evaluation modes
Llm As Judge, Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
Moderate
Use this page as
Background context only

Research brief

Metadata summary

We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task.

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

Key takeaways

  • We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task.
  • Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German.
  • It simulates a cross-lingual information retrieval scenario where questions are posed in English against all three language versions of the manual.

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

Contribution summary

  • We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task.
  • Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German.
  • The evaluation focuses on realistic "needle-in-a-haystack" challenges and includes unanswerable questions to test for hallucinations.

Why it matters for eval

  • Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German.
  • The evaluation focuses on realistic "needle-in-a-haystack" challenges and includes unanswerable questions to test for hallucinations.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Llm As Judge, Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Needle In A Haystack

  • Metric reporting is present

    Detected: accuracy